JSDM models test
Data description
Data was simulated using VirtualCommunity code.
Simulated data contains 20 data sets.
Models : Po Models were fitted for 2 datasets :
1.Env5Sp - environmental filetering only for 5 species (asssuming no interactions).
2.FacDen10Sp - environmental filetering + facilitation for 10 species.
me5 <- load_object("model-2019-04-09-19-02-16.rda")
#load("model-2019-04-09-19-02-16.rda")
summary(me5)## Summary for model '/var/folders/2z/0sxx_8ts2pxcp028sy41wfgc0000gn/T//RtmpeNqPAL/file1219c690e12c'
## Saved parameters: B Rho EnvRho Tau
## MCMC ran in parallel for 34.541 minutes at time 2019-04-09 18:27:42.
##
## For each of 5 chains:
## Adaptation: 250000 iterations (possibly insufficient)
## Burn-in: 0 iterations
## Thin rate: 100 iterations
## Total chain length: 270000 iterations
## Posterior sample size: 200 draws
##
## **WARNING** Rhat values indicate convergence failure.me5$Rhat## $B
## [,1] [,2] [,3]
## [1,] 1.135989 1.018197 1.1245544
## [2,] 1.013833 1.004344 1.0112301
## [3,] 1.002576 1.001542 0.9994323
## [4,] 1.031917 1.004833 1.0323277
## [5,] 1.018700 1.015160 1.0197400
##
## $Rho
## [,1] [,2] [,3] [,4] [,5]
## [1,] 0.9976643 1.039458 1.052623 1.0094656 1.015226
## [2,] 1.0394584 0.997953 1.026200 1.0177929 1.007785
## [3,] 1.0526226 1.026200 0.997928 1.0144650 1.063094
## [4,] 1.0094656 1.017793 1.014465 0.9979433 1.015443
## [5,] 1.0152264 1.007785 1.063094 1.0154435 NA
##
## $EnvRho
## [,1] [,2] [,3] [,4] [,5]
## [1,] 0.9976838 1.0673883 1.0948150 1.0885041 1.1003815
## [2,] 1.0673883 0.9977664 1.0114810 1.0220815 1.0142215
## [3,] 1.0948150 1.0114810 0.9982452 1.0139774 1.0122185
## [4,] 1.0885041 1.0220815 1.0139774 0.9977469 1.0163906
## [5,] 1.1003815 1.0142215 1.0122185 1.0163906 0.9980662
##
## $Tau
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1.469444 1.020321 1.044180 1.030605 1.015437
## [2,] 1.020321 1.140789 1.011127 1.051937 1.012689
## [3,] 1.044180 1.011127 1.124484 1.011076 1.019733
## [4,] 1.030605 1.051937 1.011076 1.195892 1.012849
## [5,] 1.015437 1.012689 1.019733 1.012849 1.063968me5$n.eff## $B
## [,1] [,2] [,3]
## [1,] 29 155 32
## [2,] 257 547 283
## [3,] 756 1000 1000
## [4,] 103 593 94
## [5,] 181 204 177
##
## $Rho
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1 86 66 284 232
## [2,] 86 1 132 204 350
## [3,] 66 132 1 195 53
## [4,] 284 204 195 1 195
## [5,] 232 350 53 195 1
##
## $EnvRho
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1 54 39 53 48
## [2,] 54 1 231 129 277
## [3,] 39 231 1 189 273
## [4,] 53 129 189 1 181
## [5,] 48 277 273 181 1
##
## $Tau
## [,1] [,2] [,3] [,4] [,5]
## [1,] 12 167 129 116 376
## [2,] 167 29 411 82 216
## [3,] 129 411 33 491 159
## [4,] 116 82 491 21 251
## [5,] 376 216 159 251 59me5$mcmc.info[1:7]## $n.chains
## [1] 5
##
## $n.adapt
## [1] 250000 250000 250000 250000 250000
##
## $sufficient.adapt
## [1] FALSE FALSE FALSE FALSE FALSE
##
## $n.iter
## [1] 20000
##
## $n.burnin
## [1] 0
##
## $n.thin
## [1] 100
##
## $n.samples
## [1] 1000Environment filtering 5 species
JSDM
Model for \(\textbf{EnvEvenSp5}\)
data<-sim_data$EnvEvenSp5
data <- list(
Y = subset(data, select = -env),
X = cbind(1, scale(poly(data$env, 2))),
covx = cov(cbind(1, scale(poly(data$env, 2)))),
K = 3,
J = ncol(data) - 1,
n = nrow(data),
I = diag(ncol(data) - 1),
df = ncol(data)
)
Y_cor<-cor(data$Y)
to_prec<-function(m){
n<-dim(m)[1]
Tau_n<-matrix(nrow=n, ncol=n)
for (j in 1:n) {
for (k in 1:n){
Tau_n[j, k] <- -m[j, k]/sqrt((m[j,j]*m[k,k]))
}
}
return(Tau_n)
}
#Tau_n<-matrix(nrow=dim(model$mean$Tau)[1], ncol=dim(model$mean$Tau)[1])
Tau_n<-to_prec(me5$mean$Tau)
#Tau_k<-Tau_n*(!(model$q97.5$Tau>0 & model$q2.5$Tau<0))
par(mfrow=c(2,4),oma = c(3, 1, 2, 1))
corrplot(Y_cor, diag = FALSE, order = "original",tl.pos = "ld", tl.cex = 0.5, method = "color", type = "lower")
title("Correlation cor(Y)")
corrplot(me5$mean$EnvRho, diag = FALSE, order = "original",tl.pos = "ld", tl.cex = 0.5, method = "color", type = "lower")
title("EnvRho")
corrplot(me5$mean$EnvRho*(!me5$overlap0$EnvRho), diag = FALSE, order = "original",tl.pos = "ld", tl.cex = 0.5, method = "color", type = "lower")
title("EnvRho signif")
corrplot(me5$mean$Rho, diag = FALSE, order = "original",tl.pos = "ld", tl.cex = 0.5, method = "color", type = "lower")
title("Rho")
corrplot(me5$mean$Rho*(!me5$overlap0$Rho), diag = FALSE, order = "original",tl.pos = "ld", tl.cex = 0.5, method = "color", type = "lower")
title("Rho signif")
corrplot(Tau_n, diag = FALSE, order ="original",tl.pos = "ld", tl.cex = 0.5, method = "color", type = "lower")
title("Tau")
corrplot(Tau_n*(!me5$overlap0$Tau), diag = FALSE, order ="original",tl.pos = "ld", tl.cex = 0.5, method = "color", type = "lower")
title("Tau")
GJAM
############################################################Functions
makeSymm <- function(m) {
m[upper.tri(m)] <- t(m)[upper.tri(m)]
return(m)
}
convert_to_m<-function(ar){
d <-floor((sqrt(length(ar)*8+1)-1)/2)
C <- matrix(0,d,d)
i.lwr <- which(lower.tri(C, diag = TRUE), arr.ind=TRUE)
C[i.lwr] <- ar
C<-makeSymm(C)
return(t(C))
}
fit_gjam<-function(data, it=2500,burn=500 , name="./gjam_models/temp.rda",interact=diag(ncol(data$Y))){
#setup parameters
data <- list(
Y = subset(data, select = -env),
X = cbind(1, scale(poly(data$env, 2))),
covx = cov(cbind(1, scale(poly(data$env, 2)))),
K = 3,
J = ncol(data) - 1,
n = nrow(data),
I = diag(ncol(data) - 1),
df = ncol(data)
)
xdata<-as.data.frame(data$X[,-1])
colnames(xdata)<- c("env","env2")
ydata<-as.data.frame(data$Y)
###formula
formula<-as.formula( ~env+ env2)
ml <- list(ng = it, burnin = burn, typeNames = 'PA')
####fit
mod_gjam1 <- gjam(formula, xdata = xdata, ydata = ydata, modelList = ml)
save(mod_gjam1, file = name)
summary(mod_gjam1)
Tau <- solve(mod_gjam1$parameters$sigMu)
Tau_n = to_prec(Tau)
postH<-apply(mod_gjam1$chains$sgibbs, 2, quantile,0.95)
postL<-apply(mod_gjam1$chains$sgibbs, 2, quantile,0.05)
pH<-convert_to_m(postH)
pL<-convert_to_m(postL)
R_sign<-cov2cor(mod_gjam1$parameters$sigMu)*(!(pH>0 & pL<0))
par(mfrow=c(2,3),oma = c(1, 1, 1, 1))
corrplot(cor(ydata), diag = FALSE, order = "original",tl.pos = "ld", tl.cex = 0.5, method = "color", type = "lower")
title("Correlation cor(Y)")
corrplot(mod_gjam1$parameters$corMu, diag = FALSE, order = "original",tl.pos = "ld", tl.cex = 0.5, method = "color", type = "lower")
title("R")
corrplot(mod_gjam1$parameters$ematrix, diag = FALSE, order = "original",tl.pos = "ld", tl.cex = 0.5, method = "color", type = "lower")
title("E matrix")
corrplot(Tau_n, diag = FALSE, order = "original",tl.pos = "ld", tl.cex = 0.5, method = "color", type = "lower")
title("Tau")
corrplot(R_sign, diag = FALSE, order = "original",tl.pos = "ld", tl.cex = 0.5, method = "color", type = "lower")
title("R signif")
corrplot(interact, diag = FALSE, order = "original",tl.pos = "ld", tl.cex = 0.5, method = "color", type = "lower")
title("True interactions")
}
load_gjam<-function(data,name="./gjam_models/temp.rda",interact=diag(ncol(data$Y))){
#setup parameters
data <- list(
Y = subset(data, select = -env),
X = cbind(1, scale(poly(data$env, 2))),
covx = cov(cbind(1, scale(poly(data$env, 2)))),
K = 3,
J = ncol(data) - 1,
n = nrow(data),
I = diag(ncol(data) - 1),
df = ncol(data)
)
gj_mod<-load_object(name)
summary(gj_mod)
Tau <- solve(gj_mod$parameters$sigMu)
Tau_n = to_prec(Tau)
postH<-apply(gj_mod$chains$sgibbs, 2, quantile,0.95)
postL<-apply(gj_mod$chains$sgibbs, 2, quantile,0.05)
pH<-convert_to_m(postH)
pL<-convert_to_m(postL)
R_sign<-cov2cor(gj_mod$parameters$sigMu)*(!(pH>0 & pL<0))
par(mfrow=c(2,3),oma = c(1, 1, 1, 1))
corrplot(cor(data$Y), diag = FALSE, order = "original",tl.pos = "ld", tl.cex = 0.5, method = "color", type = "lower")
title("Correlation cor(Y)")
corrplot(gj_mod$parameters$corMu, diag = FALSE, order = "original",tl.pos = "ld", tl.cex = 0.5, method = "color", type = "lower")
title("R")
corrplot(gj_mod$parameters$ematrix, diag = FALSE, order = "original",tl.pos = "ld", tl.cex = 0.5, method = "color", type = "lower")
title("E matrix")
corrplot(Tau_n, diag = FALSE, order = "original",tl.pos = "ld", tl.cex = 0.5, method = "color", type = "lower")
title("Tau")
corrplot(R_sign, diag = FALSE, order = "original",tl.pos = "ld", tl.cex = 0.5, method = "color", type = "lower")
title("R signif")
corrplot(interact, diag = FALSE, order = "original",tl.pos = "ld", tl.cex = 0.5, method = "color", type = "lower")
title("True interactions")
}
######################################################################
data<-sim_data$EnvEvenSp5
#fit_gjam(data,2000,1000,"./gjam_models/gjam5env.rda")
load_gjam(data,name="./gjam_models/gjam5env.rda")##
## Sensitivity by predictor variables f:
## Estimate SE CI_025 CI_975
## env 9.5 3.13 5.08 16.4
## env2 31.9 7.93 20.50 50.6
##
## Coefficient matrix B:
## sp01 sp02 sp03 sp04 sp05
## intercept -0.0312 0.646 1.2500 0.704 -1.200
## env -2.4200 -1.800 0.0438 1.710 2.870
## env2 0.9440 -0.527 -0.9410 -0.598 -0.665
## RMSPE 0.2780 0.327 0.3760 0.309 0.327
##
## Coefficient matrix B:
## Estimate SE CI_025 CI_975 sig95
## sp01_intercept -0.0312 0.0775 -0.1940 0.107
## sp01_env -2.4200 0.1170 -2.6500 -2.200 *
## sp01_env2 0.9440 0.0938 0.7780 1.130 *
## sp02_intercept 0.6460 0.0651 0.5220 0.771 *
## sp02_env -1.8000 0.0911 -1.9900 -1.630 *
## sp02_env2 -0.5270 0.0741 -0.6700 -0.389 *
## sp03_intercept 1.2500 0.0672 1.1100 1.380 *
## sp03_env 0.0438 0.0465 -0.0447 0.132
## sp03_env2 -0.9410 0.0764 -1.0900 -0.781 *
## sp04_intercept 0.7040 0.0865 0.5590 0.881 *
## sp04_env 1.7100 0.0897 1.5500 1.890 *
## sp04_env2 -0.5980 0.0608 -0.7170 -0.486 *
## sp05_intercept -1.2000 0.0953 -1.3700 -1.000 *
## sp05_env 2.8700 0.1380 2.6300 3.170 *
## sp05_env2 -0.6650 0.0610 -0.7840 -0.545 *
##
## Last column indicates if 95% posterior distribution contains zero.
##
## Coefficient matrix B, standardized for X:
## NULL
##
## Last column indicates if 95% posterior distribution contains zero.
##
## Coefficient matrix B, standardized for X and W:
## NULL
##
## Last column indicates if 95% posterior distribution contains zero.
##
## Design Table
## env env2
## VIF 1 1
## env2 0 NA
##
## Sample contains n = 500 observations on S = 5 response variables, and 2 predictors. Data types (typeNames) include PA. There are 0 missing values in X and 0 missing values in Y. The RMSPE is 0.325, and the DIC is 25294. Computation involved 2000 Gibbs steps, with a burnin of 1000.
HMSC
#setwd("~/Tesi/Code/Ecology-models-master/simcoms-master/ExampleFiles")
fit_hmsc<-function(data,label="F_t",nsamples = 1000,nchains=2,name="./HMmodels/hmtemp.rda" ){
if (label=="F_t"){
Y_data = subset(data, select = -env)
ns<- ncol(Y_data)
np <- nrow(Y_data)
X<-scale(poly(data$env[1:np], 2))
colnames(X)<-c("env","env2")
studyDesign = data.frame(sample = as.factor(1:np))
rL = HmscRandomLevel(units = studyDesign$sample)
m = Hmsc(Y=as.matrix(Y_data), XData=as.data.frame(X), XFormula=~env+env2, distr="probit",
studyDesign = studyDesign, ranLevels = list(sample = rL))
m = sampleMcmc(m, nsamples, thin=10, adaptNf=c(200,200), transient=500,nChains=nchains ,verbose=F)
save(m, file = name)
return(m)
}
if (label=="L_d"){
return(load_object(name))
}
}
data<-sim_data$EnvEvenSp5
hm_mod<-fit_hmsc(data,"F_t",name="./HMmodels/hm5env.rda" )
#hm_mod<-load_object("./HMmodels/hm5env.rda")Convergence:
hm_conv<-function(mod){
codaList = convertToCodaObject(mod)
#convergence histograms
hist(effectiveSize(codaList$Beta), main="ess(beta)")
hist(gelman.diag(codaList$Beta,multivariate=FALSE)$psrf, main="psrf(beta)")
hist(effectiveSize(codaList$Omega[[1]]), main="ess(omega)")
hist(gelman.diag(codaList$Omega[[1]], multivariate=FALSE)$psrf, main="psrf(omega)")
}
hm_conv(hm_mod)
Study of interactions
hm_inter<-function(mod, nchains=2,nsamples = 1000, interact=diag(ns)){
getOmega = function(a,r=1)
return(crossprod(a$Lambda[[r]]))
ns<-mod$ns
postOmega1 = array(unlist(lapply(mod$postList[[1]],getOmega)),c(ns,ns,mod$samples))
postOmega2 = array(unlist(lapply(mod$postList[[2]],getOmega)),c(ns,ns,mod$samples))
postOmega<-abind(postOmega1,postOmega2,along=3)
postOmegaMean = apply(postOmega,c(1,2),mean)
postOmegaUp=apply(postOmega,c(1,2),quantile,0.95)
postOmegaLo=apply(postOmega,c(1,2),quantile,0.05)
postR<-array(dim=c(ns,ns,nchains*nsamples))
for(i in 1:dim(postOmega)[3])
postR[,,i]<-stats::cov2cor(postOmega[,,i])
postRMean = apply(postR,c(1,2),mean)
postRUp=apply(postR,c(1,2),quantile,0.95)
postRLo=apply(postR,c(1,2),quantile,0.05)
Tau = solve(postOmegaMean)
Tau_n = cov2cor(Tau)
Toplot_R<-postRMean*(!(postRUp>0 & postRLo<0))
# Omegacor<- computeAssociations(m)
# supportLevel<- 0.95
# toPlot<- ((Omegacor[[1]]$support>supportLevel)+ (Omegacor[[1]]$support<(1-supportLevel))>0)*Omegacor[[1]]$mean
# corrplot(toPlot, method="color", col=colorRampPalette(c("blue", "white", "red"))(200))
par(mfrow=c(2,3),oma = c(1, 1, 1, 1))
corrplot(cor(hm_mod$Y), diag = FALSE, order = "FPC",tl.pos = "ld", tl.cex = 0.5, method = "color", type = "lower")
title("Correlation cor(Y)")
corrplot(postRMean, diag = FALSE, order = "FPC",tl.pos = "ld", tl.cex = 0.5, method = "color", type = "lower")
title("R")
corrplot(Toplot_R, diag = FALSE, order = "FPC",tl.pos = "ld", tl.cex = 0.5, method = "color", type = "lower")
title("Plot only non zero value")
corrplot(Tau_n, diag = FALSE, order = "FPC",tl.pos = "ld", tl.cex = 0.5, method = "color", type = "lower")
title("Partial correlation matrix")
corrplot(interact, diag = FALSE, order = "FPC",tl.pos = "ld", tl.cex = 0.5, method = "color", type = "lower")
title("True interactions")
}
hm_inter(hm_mod)
Environmental filtering 10 species
JSDM
me10 <- load_object("model-2019-04-10-08-26-20.rda")
#load("model-2019-04-09-19-02-16.rda")
summary(me10)## Summary for model '/tmp/RtmpKix4lZ/file40e957831178'
## Saved parameters: B Rho EnvRho
## MCMC ran in parallel for 683.734 minutes at time 2019-04-09 21:02:35.
##
## For each of 5 chains:
## Adaptation: 250000 iterations (possibly insufficient)
## Burn-in: 0 iterations
## Thin rate: 100 iterations
## Total chain length: 270000 iterations
## Posterior sample size: 200 draws
##
## **WARNING** Rhat values indicate convergence failure.me10$Rhat## $B
## [,1] [,2] [,3]
## [1,] 1.120326 1.068731 1.105596
## [2,] 1.120646 1.023810 1.117608
## [3,] 1.025126 1.021987 1.039450
## [4,] 1.009218 1.002054 1.003472
## [5,] 1.001461 1.003377 1.000483
## [6,] 1.002240 1.004165 1.001141
## [7,] 1.002028 1.005003 1.010800
## [8,] 1.068841 1.052336 1.061238
## [9,] 1.076116 1.070068 1.072570
## [10,] 1.453481 1.076523 1.560232
##
## $Rho
## [,1] [,2] [,3] [,4] [,5] [,6] [,7]
## [1,] 0.9978526 1.0290591 1.0678412 1.0599436 1.0399056 1.028928 1.0507803
## [2,] 1.0290591 0.9982297 1.0197007 1.0252205 1.0401715 1.021111 1.0172827
## [3,] 1.0678412 1.0197007 0.9987326 1.0059132 1.0715726 1.114560 1.0315975
## [4,] 1.0599436 1.0252205 1.0059132 0.9992124 1.0086992 1.040600 1.0561948
## [5,] 1.0399056 1.0401715 1.0715726 1.0086992 0.9986616 1.011374 1.0423583
## [6,] 1.0289278 1.0211114 1.1145598 1.0405996 1.0113744 0.999231 1.0127345
## [7,] 1.0507803 1.0172827 1.0315975 1.0561948 1.0423583 1.012734 0.9988621
## [8,] 1.0037681 1.0121168 1.0453821 1.0539626 1.0384907 1.010776 1.0065758
## [9,] 1.0253918 1.0149548 1.0102800 1.1800843 1.0542274 1.023794 1.0145598
## [10,] 1.0160857 1.0121523 1.0122942 1.0110807 1.0574194 1.041111 1.1259440
## [,8] [,9] [,10]
## [1,] 1.0037681 1.025392 1.016086
## [2,] 1.0121168 1.014955 1.012152
## [3,] 1.0453821 1.010280 1.012294
## [4,] 1.0539626 1.180084 1.011081
## [5,] 1.0384907 1.054227 1.057419
## [6,] 1.0107757 1.023794 1.041111
## [7,] 1.0065758 1.014560 1.125944
## [8,] 0.9984035 1.097070 1.049173
## [9,] 1.0970703 1.000262 1.021051
## [10,] 1.0491732 1.021051 NA
##
## $EnvRho
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 0.9989701 1.0915613 1.0494713 1.0476542 1.0612422 1.0933407
## [2,] 1.0915613 0.9980448 1.1008798 1.0605411 1.0774669 1.0677039
## [3,] 1.0494713 1.1008798 0.9977531 1.0270558 1.0223152 1.0271401
## [4,] 1.0476542 1.0605411 1.0270558 0.9985439 1.0055161 1.0087761
## [5,] 1.0612422 1.0774669 1.0223152 1.0055161 0.9980406 1.0033713
## [6,] 1.0933407 1.0677039 1.0271401 1.0087761 1.0033713 0.9976375
## [7,] 1.1032291 1.0851283 1.0274782 1.0126996 0.9996139 1.0027872
## [8,] 1.1457862 1.0409139 1.0640407 1.0501193 1.0278626 1.0321936
## [9,] 1.0633837 1.0215345 1.0115341 1.0424066 1.0311304 1.0386605
## [10,] 1.2623168 1.7953449 1.1707963 1.3153329 1.2841446 1.3435169
## [,7] [,8] [,9] [,10]
## [1,] 1.1032291 1.1457862 1.0633837 1.2623168
## [2,] 1.0851283 1.0409139 1.0215345 1.7953449
## [3,] 1.0274782 1.0640407 1.0115341 1.1707963
## [4,] 1.0126996 1.0501193 1.0424066 1.3153329
## [5,] 0.9996139 1.0278626 1.0311304 1.2841446
## [6,] 1.0027872 1.0321936 1.0386605 1.3435169
## [7,] 0.9983449 1.0312640 1.0319857 1.3542085
## [8,] 1.0312640 0.9994923 1.1180936 1.2880511
## [9,] 1.0319857 1.1180936 0.9980654 1.1956412
## [10,] 1.3542085 1.2880511 1.1956412 0.9976116me10$n.eff## $B
## [,1] [,2] [,3]
## [1,] 34 51 38
## [2,] 33 121 31
## [3,] 130 137 82
## [4,] 271 1000 645
## [5,] 824 593 1000
## [6,] 756 670 1000
## [7,] 729 670 259
## [8,] 58 69 61
## [9,] 45 47 47
## [10,] 11 47 10
##
## $Rho
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 1 119 49 54 84 102 66 519 121 186
## [2,] 119 1 162 121 81 136 184 283 224 219
## [3,] 49 162 1 755 53 34 120 72 256 217
## [4,] 54 121 755 1 357 80 82 75 22 273
## [5,] 84 81 53 357 1 269 76 84 62 59
## [6,] 102 136 34 80 269 1 249 404 136 78
## [7,] 66 184 120 82 76 249 1 391 363 30
## [8,] 519 283 72 75 84 404 391 1 36 66
## [9,] 121 224 256 22 62 136 363 36 1 157
## [10,] 186 219 217 273 59 78 30 66 157 1
##
## $EnvRho
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 1 60 74 80 59 39 39 34 89 18
## [2,] 60 1 36 56 44 49 39 86 230 9
## [3,] 74 36 1 144 134 112 110 51 249 23
## [4,] 80 56 144 1 505 379 214 67 74 14
## [5,] 59 44 134 505 1 632 1000 113 106 16
## [6,] 39 49 112 379 632 1 801 103 86 14
## [7,] 39 39 110 214 1000 801 1 128 102 14
## [8,] 34 86 51 67 113 103 128 1 42 17
## [9,] 89 230 249 74 106 86 102 42 1 23
## [10,] 18 9 23 14 16 14 14 17 23 1me10$mcmc.info[1:7]## $n.chains
## [1] 5
##
## $n.adapt
## [1] 250000 250000 250000 250000 250000
##
## $sufficient.adapt
## [1] FALSE FALSE FALSE FALSE FALSE
##
## $n.iter
## [1] 20000
##
## $n.burnin
## [1] 0
##
## $n.thin
## [1] 100
##
## $n.samples
## [1] 1000models<-list(EnvEvenSp10=me10)
mean_correlations<-mean_cor(models)
x <- subset(mean_correlations, type != "Environmental\nFiltering Only")
acc <- by(x, x$model, function(x) sum(x$status == "TP" | x$status == "TN") / nrow(x))
#' Plot correlation parameter means
#+ plot-correlations
ggplot(mean_correlations) +
aes(factor(nsp), rho, fill = interaction) +
geom_hline(yintercept = 0) +
geom_boxplot(
outlier.size = .2, size = .1, position = position_dodge(preserve = "single")
) +
scale_fill_manual(values = c("grey", "blue", "red")) +
facet_grid(type ~ rho_type + density, switch = "y") +
xlab("Number of species") +
ylab("Correlation") +
theme_bw() +
theme(legend.position = "top")
data<-sim_data$EnvEvenSp10
data <- list(
Y = subset(data, select = -env),
X = cbind(1, scale(poly(data$env, 2))),
covx = cov(cbind(1, scale(poly(data$env, 2)))),
K = 3,
J = ncol(data) - 1,
n = nrow(data),
I = diag(ncol(data) - 1),
df = ncol(data)
)
##########################################################################################
#Tau<-solve
#Tau_n<-to_prec(me10$mean$Tau)
#Tau_k<-Tau_n*(!(model$q97.5$Tau>0 & model$q2.5$Tau<0))
plot_cor_jsdm<-function(mod,y,interact=diag(ncol(y))){
par(mfrow=c(2,4),oma = c(3, 1, 2, 1))
corrplot(cor(y), diag = FALSE, order = "original",tl.pos = "ld", tl.cex = 0.5, method = "color", type = "lower")
title("Correlation cor(Y)")
corrplot(mod$mean$EnvRho, diag = FALSE, order = "original",tl.pos = "ld", tl.cex = 0.5, method = "color", type = "lower")
title("EnvRho")
corrplot(mod$mean$EnvRho*(!mod$overlap0$EnvRho), diag = FALSE, order = "original",tl.pos = "ld", tl.cex = 0.5, method = "color", type = "lower")
title("EnvRho signif")
corrplot(mod$mean$Rho, diag = FALSE, order = "original",tl.pos = "ld", tl.cex = 0.5, method = "color", type = "lower")
title("Rho")
corrplot(mod$mean$Rho*(!mod$overlap0$Rho), diag = FALSE, order = "original",tl.pos = "ld", tl.cex = 0.5, method = "color", type = "lower")
title("Rho signif")
corrplot(interact, diag = FALSE, order = "original",tl.pos = "ld", tl.cex = 0.5, method = "color", type = "lower")
title("True interactions")
#corrplot(Tau_n, diag = FALSE, order ="original",tl.pos = "ld", tl.cex = 0.5, method = "color", type = "lower")
#title("Tau")
#corrplot(Tau_n*(!me10$overlap0$Tau), diag = FALSE, order ="original",tl.pos = "ld", tl.cex = 0.5, method = "color", type = "lower")
#title("Tau")
}
plot_cor_jsdm(me10,data$Y)
Gjam
data<-sim_data$EnvEvenSp10
#fit_gjam(data,5000,500,"./gjam_models/gjam10env.rda")
load_gjam(data,name="./gjam_models/gjam10env.rda")##
## Sensitivity by predictor variables f:
## Estimate SE CI_025 CI_975
## env 233.0 58.0 120 354.0
## env2 56.7 11.8 37 82.9
##
## Coefficient matrix B:
## sp01 sp02 sp03 sp04 sp05 sp06 sp07 sp08 sp09
## intercept -0.2060 0.3800 1.310 0.889 1.020 0.742 0.638 0.140 0.332
## env -2.0500 -2.5700 -2.300 -1.030 -0.189 0.542 1.090 1.740 2.520
## env2 0.0656 -0.0531 0.338 -0.533 -0.805 -0.838 -0.690 -0.425 0.640
## RMSPE 0.3300 0.2980 0.317 0.371 0.414 0.421 0.374 0.345 0.298
## sp10
## intercept -0.577
## env 2.370
## env2 0.677
## RMSPE 0.276
##
## Coefficient matrix B:
## Estimate SE CI_025 CI_975 sig95
## sp01_intercept -0.2060 0.0670 -0.33700 -0.0733 *
## sp01_env -2.0500 0.1190 -2.29000 -1.8300 *
## sp01_env2 0.0656 0.0992 -0.14100 0.2380
## sp02_intercept 0.3800 0.1030 0.18000 0.5510 *
## sp02_env -2.5700 0.1370 -2.83000 -2.2900 *
## sp02_env2 -0.0531 0.0790 -0.19700 0.1120
## sp03_intercept 1.3100 0.0829 1.15000 1.4700 *
## sp03_env -2.3000 0.1260 -2.55000 -2.0600 *
## sp03_env2 0.3380 0.0712 0.20500 0.4860 *
## sp04_intercept 0.8890 0.0585 0.77400 1.0000 *
## sp04_env -1.0300 0.0659 -1.16000 -0.9070 *
## sp04_env2 -0.5330 0.0580 -0.64200 -0.4160 *
## sp05_intercept 1.0200 0.0621 0.90800 1.1500 *
## sp05_env -0.1890 0.0488 -0.28500 -0.0949 *
## sp05_env2 -0.8050 0.0759 -0.96000 -0.6590 *
## sp06_intercept 0.7420 0.0635 0.61600 0.8650 *
## sp06_env 0.5420 0.0558 0.43200 0.6460 *
## sp06_env2 -0.8380 0.0634 -0.96100 -0.7130 *
## sp07_intercept 0.6380 0.0795 0.50600 0.8160 *
## sp07_env 1.0900 0.0805 0.93900 1.2500 *
## sp07_env2 -0.6900 0.0595 -0.80300 -0.5700 *
## sp08_intercept 0.1400 0.0759 0.00798 0.2950 *
## sp08_env 1.7400 0.1240 1.48000 1.9600 *
## sp08_env2 -0.4250 0.1000 -0.65400 -0.2750 *
## sp09_intercept 0.3320 0.0539 0.22600 0.4370 *
## sp09_env 2.5200 0.1660 2.22000 2.8500 *
## sp09_env2 0.6400 0.0700 0.50400 0.7780 *
## sp10_intercept -0.5770 0.0696 -0.70200 -0.4380 *
## sp10_env 2.3700 0.1130 2.16000 2.6000 *
## sp10_env2 0.6770 0.0820 0.49900 0.8200 *
##
## Last column indicates if 95% posterior distribution contains zero.
##
## Coefficient matrix B, standardized for X:
## NULL
##
## Last column indicates if 95% posterior distribution contains zero.
##
## Coefficient matrix B, standardized for X and W:
## NULL
##
## Last column indicates if 95% posterior distribution contains zero.
##
## Design Table
## env env2
## VIF 1 1
## env2 0 NA
##
## Sample contains n = 500 observations on S = 10 response variables, and 2 predictors. Data types (typeNames) include PA. There are 0 missing values in X and 0 missing values in Y. The RMSPE is 0.348, and the DIC is 114934. Computation involved 5000 Gibbs steps, with a burnin of 500.
#gje10<-load_object("./gjam_models/gjam10env.rda")
#to check posterior density of s in Sigma
#gje10<-load_object("./gjam_models/gjam10env.rda")
#plot(density(gje10$chains$sgibbs[,4]))HMSC
data<-sim_data$EnvEvenSp10
#hm_mod<-fit_hmsc(data,"F_t",name="./HMmodels/hm10env.rda" )
hm_mod<-fit_hmsc(data,"L_d",name="./HMmodels/hm10env.rda" )
hm_conv(hm_mod)
hm_inter(hm_mod)
Environmental filtering 20 species
JSDM
me20 <- load_object("model-2019-04-11-19-06-02.rda")
#load("model-2019-04-09-19-02-16.rda")
summary(me20)## Summary for model '/tmp/RtmpKix4lZ/file40e966e51ba7'
## Saved parameters: B Rho EnvRho
## MCMC ran in parallel for 2079.661 minutes at time 2019-04-10 08:26:21.
##
## For each of 5 chains:
## Adaptation: 250000 iterations (possibly insufficient)
## Burn-in: 0 iterations
## Thin rate: 100 iterations
## Total chain length: 270000 iterations
## Posterior sample size: 200 draws
##
## **WARNING** Rhat values indicate convergence failure.me20$Rhat## $B
## [,1] [,2] [,3]
## [1,] 1.0442706 1.029720 1.0361972
## [2,] 1.0952877 1.034897 1.1269662
## [3,] 1.1802136 1.032501 1.1923295
## [4,] 1.1676379 1.127438 1.1730872
## [5,] 1.1455795 1.072972 1.1271668
## [6,] 1.0378201 1.020059 1.0403266
## [7,] 1.0966605 1.108003 1.0904722
## [8,] 1.0030607 1.014022 1.0370032
## [9,] 0.9987167 1.009828 0.9990432
## [10,] 1.0037613 1.001993 1.0118251
## [11,] 1.0026689 1.008913 1.0000461
## [12,] 1.0159976 1.006302 1.0073405
## [13,] 1.0051811 1.006104 1.0080195
## [14,] 1.0074564 1.025066 1.0196677
## [15,] 1.0795886 1.092827 1.0576292
## [16,] 1.0640851 1.030574 1.0506423
## [17,] 1.1797437 1.075497 1.1578266
## [18,] 1.0886486 1.061220 1.1351973
## [19,] 1.2173736 1.047060 1.1899919
## [20,] 1.1109659 1.027156 1.1309502
##
## $Rho
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 0.9998231 1.0160280 1.0305822 1.0279291 1.0473367 1.0780718
## [2,] 1.0160280 0.9978814 1.0431386 1.0161541 1.0302730 1.2494328
## [3,] 1.0305822 1.0431386 0.9995131 1.0424140 1.0801828 1.1332232
## [4,] 1.0279291 1.0161541 1.0424140 0.9978649 1.0078637 1.0195954
## [5,] 1.0473367 1.0302730 1.0801828 1.0078637 0.9978405 1.0057194
## [6,] 1.0780718 1.2494328 1.1332232 1.0195954 1.0057194 0.9989582
## [7,] 1.0447842 1.0187091 1.2106201 1.0122538 1.0231265 1.0024899
## [8,] 1.0352789 1.0199386 1.0543410 1.0274196 1.0308456 1.0430306
## [9,] 1.0263952 1.0567748 1.0251599 1.0435112 1.0180063 1.0212570
## [10,] 1.1596349 1.0866691 1.0261957 1.0859807 1.0081510 1.0330128
## [11,] 1.0688870 1.0270544 1.0385418 1.0576555 1.0163509 1.0028639
## [12,] 1.0546815 1.0472973 1.0351153 1.0525365 1.0666341 1.0429530
## [13,] 1.0172979 1.0987287 1.0507849 1.1504208 1.0817905 1.0506452
## [14,] 1.0133158 1.0301913 1.1696609 1.0086273 1.0863943 1.0113239
## [15,] 1.0278406 1.0242104 1.0577668 1.0085084 1.0621628 1.1185167
## [16,] 1.0427642 1.0231871 1.0566623 1.0653141 1.0452490 1.0761694
## [17,] 1.0321550 1.0370350 1.0481609 1.0622793 1.0465552 1.0191210
## [18,] 1.0118686 1.0108567 1.0583566 1.0487757 1.0125671 1.0215094
## [19,] 1.0504864 1.0127250 1.0239930 1.0279518 1.0246607 1.0263601
## [20,] 1.0227614 1.0091419 1.0259128 1.0057442 1.0018726 1.0152869
## [,7] [,8] [,9] [,10] [,11] [,12]
## [1,] 1.0447842 1.0352789 1.0263952 1.1596349 1.0688870 1.0546815
## [2,] 1.0187091 1.0199386 1.0567748 1.0866691 1.0270544 1.0472973
## [3,] 1.2106201 1.0543410 1.0251599 1.0261957 1.0385418 1.0351153
## [4,] 1.0122538 1.0274196 1.0435112 1.0859807 1.0576555 1.0525365
## [5,] 1.0231265 1.0308456 1.0180063 1.0081510 1.0163509 1.0666341
## [6,] 1.0024899 1.0430306 1.0212570 1.0330128 1.0028639 1.0429530
## [7,] 0.9978295 1.0210246 1.0209097 1.0095723 1.0332082 1.0431261
## [8,] 1.0210246 0.9997523 1.0313886 1.0072462 1.0174961 1.0266471
## [9,] 1.0209097 1.0313886 0.9979056 1.0022275 1.0222953 1.0217759
## [10,] 1.0095723 1.0072462 1.0022275 0.9990841 1.0279547 1.0478724
## [11,] 1.0332082 1.0174961 1.0222953 1.0279547 0.9982616 1.0053502
## [12,] 1.0431261 1.0266471 1.0217759 1.0478724 1.0053502 0.9985451
## [13,] 1.0303016 1.0278093 1.0366570 1.0700180 1.0110195 1.0256484
## [14,] 1.1458656 1.0104807 1.0159249 1.0250766 1.0239484 1.0212415
## [15,] 1.1488222 1.0219934 1.0911735 1.0736116 1.0029024 1.0495264
## [16,] 1.0122125 1.0413815 1.0869929 1.0326952 1.0188790 1.0054973
## [17,] 1.0771514 1.0410685 1.0814933 1.0908196 1.0564717 1.0392236
## [18,] 1.0292276 1.0235426 1.0642610 1.0188853 1.0288882 1.0457003
## [19,] 1.0193236 1.0389791 1.0657548 1.1371595 1.0454457 1.0994502
## [20,] 1.0036981 1.0401853 1.0189975 1.0438252 1.0143399 1.0064904
## [,13] [,14] [,15] [,16] [,17] [,18]
## [1,] 1.0172979 1.0133158 1.0278406 1.0427642 1.0321550 1.0118686
## [2,] 1.0987287 1.0301913 1.0242104 1.0231871 1.0370350 1.0108567
## [3,] 1.0507849 1.1696609 1.0577668 1.0566623 1.0481609 1.0583566
## [4,] 1.1504208 1.0086273 1.0085084 1.0653141 1.0622793 1.0487757
## [5,] 1.0817905 1.0863943 1.0621628 1.0452490 1.0465552 1.0125671
## [6,] 1.0506452 1.0113239 1.1185167 1.0761694 1.0191210 1.0215094
## [7,] 1.0303016 1.1458656 1.1488222 1.0122125 1.0771514 1.0292276
## [8,] 1.0278093 1.0104807 1.0219934 1.0413815 1.0410685 1.0235426
## [9,] 1.0366570 1.0159249 1.0911735 1.0869929 1.0814933 1.0642610
## [10,] 1.0700180 1.0250766 1.0736116 1.0326952 1.0908196 1.0188853
## [11,] 1.0110195 1.0239484 1.0029024 1.0188790 1.0564717 1.0288882
## [12,] 1.0256484 1.0212415 1.0495264 1.0054973 1.0392236 1.0457003
## [13,] 0.9993036 1.0111998 1.0452570 1.0682243 1.1214569 1.1224935
## [14,] 1.0111998 0.9991569 1.0359574 1.0164046 1.0636890 1.0485195
## [15,] 1.0452570 1.0359574 0.9980932 1.0135542 1.0101433 1.0587125
## [16,] 1.0682243 1.0164046 1.0135542 0.9988827 1.0246203 1.0452053
## [17,] 1.1214569 1.0636890 1.0101433 1.0246203 0.9979598 1.0135007
## [18,] 1.1224935 1.0485195 1.0587125 1.0452053 1.0135007 0.9985193
## [19,] 1.1193621 1.0655888 1.0192911 1.0121892 1.0255944 1.0268571
## [20,] 1.0622450 1.0391188 1.0586143 1.0315405 1.0209481 1.0257643
## [,19] [,20]
## [1,] 1.0504864 1.022761
## [2,] 1.0127250 1.009142
## [3,] 1.0239930 1.025913
## [4,] 1.0279518 1.005744
## [5,] 1.0246607 1.001873
## [6,] 1.0263601 1.015287
## [7,] 1.0193236 1.003698
## [8,] 1.0389791 1.040185
## [9,] 1.0657548 1.018997
## [10,] 1.1371595 1.043825
## [11,] 1.0454457 1.014340
## [12,] 1.0994502 1.006490
## [13,] 1.1193621 1.062245
## [14,] 1.0655888 1.039119
## [15,] 1.0192911 1.058614
## [16,] 1.0121892 1.031541
## [17,] 1.0255944 1.020948
## [18,] 1.0268571 1.025764
## [19,] 0.9978034 1.021101
## [20,] 1.0211006 NA
##
## $EnvRho
## [,1] [,2] [,3] [,4] [,5] [,6] [,7]
## [1,] 0.997827 1.0441010 1.1493392 1.067035 1.1222785 1.0182787 1.052375
## [2,] 1.044101 0.9982144 1.0871296 1.069432 1.1055942 1.1221119 1.144165
## [3,] 1.149339 1.0871296 0.9976512 1.069019 1.1095193 1.1385559 1.088030
## [4,] 1.067035 1.0694324 1.0690189 0.998234 1.1232853 1.0703442 1.188422
## [5,] 1.122279 1.1055942 1.1095193 1.123285 0.9980054 1.1313959 1.068622
## [6,] 1.018279 1.1221119 1.1385559 1.070344 1.1313959 0.9987885 1.096782
## [7,] 1.052375 1.1441651 1.0880298 1.188422 1.0686224 1.0967819 0.998387
## [8,] 1.050973 1.1407021 1.1192415 1.143900 1.0608612 1.0445771 1.059006
## [9,] 1.026094 1.1091142 1.1202772 1.085074 1.0692282 1.0347099 1.056453
## [10,] 1.018515 1.0991969 1.1148041 1.083255 1.1005938 1.0159689 1.066786
## [11,] 1.028162 1.0799176 1.0830436 1.054385 1.0484167 1.0415429 1.052100
## [12,] 1.017811 1.0771486 1.1298051 1.069062 1.0857411 1.0112512 1.052883
## [13,] 1.022233 1.0706769 1.0874949 1.066030 1.0461111 1.0212945 1.043237
## [14,] 1.015402 1.0493700 1.1125706 1.095574 1.0747928 1.0237354 1.036797
## [15,] 1.084048 1.1237585 1.0915291 1.048040 1.1174138 1.0540293 1.046770
## [16,] 1.038491 1.0584445 1.1718432 1.019212 1.1616120 1.0184717 1.101882
## [17,] 1.088311 1.0778645 1.1424479 1.152100 1.1968877 1.0431616 1.238424
## [18,] 1.114268 1.1312396 1.1564149 1.082903 1.0558329 1.0836490 1.107912
## [19,] 1.034044 1.1233914 1.0493727 1.098512 1.0978271 1.2066123 1.088365
## [20,] 1.085652 1.2750851 1.3034938 1.137552 1.2256115 1.0498161 1.119712
## [,8] [,9] [,10] [,11] [,12] [,13]
## [1,] 1.0509727 1.0260941 1.0185147 1.0281622 1.0178113 1.0222332
## [2,] 1.1407021 1.1091142 1.0991969 1.0799176 1.0771486 1.0706769
## [3,] 1.1192415 1.1202772 1.1148041 1.0830436 1.1298051 1.0874949
## [4,] 1.1439004 1.0850742 1.0832551 1.0543853 1.0690624 1.0660298
## [5,] 1.0608612 1.0692282 1.1005938 1.0484167 1.0857411 1.0461111
## [6,] 1.0445771 1.0347099 1.0159689 1.0415429 1.0112512 1.0212945
## [7,] 1.0590061 1.0564529 1.0667857 1.0520999 1.0528831 1.0432370
## [8,] 0.9981886 1.0205666 1.0140835 1.0150619 1.0160461 1.0079195
## [9,] 1.0205666 0.9979412 1.0076255 1.0053350 1.0039684 0.9995601
## [10,] 1.0140835 1.0076255 0.9993447 1.0176658 1.0018828 1.0048962
## [11,] 1.0150619 1.0053350 1.0176658 0.9993772 1.0085249 1.0050172
## [12,] 1.0160461 1.0039684 1.0018828 1.0085249 0.9980083 1.0040642
## [13,] 1.0079195 0.9995601 1.0048962 1.0050172 1.0040642 0.9981715
## [14,] 1.0182912 1.0065231 1.0145350 1.0129238 1.0055573 1.0263698
## [15,] 1.0286417 1.0165496 1.0294713 1.0367012 1.0320610 1.0396031
## [16,] 1.0578753 1.0230808 1.0252637 1.0424447 1.0133924 1.0258492
## [17,] 1.1408138 1.1087298 1.0691109 1.1248860 1.0710791 1.0689701
## [18,] 1.0667858 1.0399875 1.0891331 1.0255222 1.0601065 1.0276681
## [19,] 1.1693710 1.1506484 1.1285483 1.1265122 1.1203244 1.1267177
## [20,] 1.1074126 1.0605111 1.0761721 1.0777079 1.0478400 1.0498025
## [,14] [,15] [,16] [,17] [,18] [,19] [,20]
## [1,] 1.0154020 1.0840476 1.0384909 1.088311 1.1142685 1.0340445 1.085652
## [2,] 1.0493700 1.1237585 1.0584445 1.077865 1.1312396 1.1233914 1.275085
## [3,] 1.1125706 1.0915291 1.1718432 1.142448 1.1564149 1.0493727 1.303494
## [4,] 1.0955736 1.0480404 1.0192119 1.152100 1.0829030 1.0985119 1.137552
## [5,] 1.0747928 1.1174138 1.1616120 1.196888 1.0558329 1.0978271 1.225611
## [6,] 1.0237354 1.0540293 1.0184717 1.043162 1.0836490 1.2066123 1.049816
## [7,] 1.0367971 1.0467703 1.1018823 1.238424 1.1079122 1.0883652 1.119712
## [8,] 1.0182912 1.0286417 1.0578753 1.140814 1.0667858 1.1693710 1.107413
## [9,] 1.0065231 1.0165496 1.0230808 1.108730 1.0399875 1.1506484 1.060511
## [10,] 1.0145350 1.0294713 1.0252637 1.069111 1.0891331 1.1285483 1.076172
## [11,] 1.0129238 1.0367012 1.0424447 1.124886 1.0255222 1.1265122 1.077708
## [12,] 1.0055573 1.0320610 1.0133924 1.071079 1.0601065 1.1203244 1.047840
## [13,] 1.0263698 1.0396031 1.0258492 1.068970 1.0276681 1.1267177 1.049803
## [14,] 0.9979319 1.0482662 1.0345494 1.088266 1.0844502 1.1132210 1.075446
## [15,] 1.0482662 0.9983997 1.0118631 1.056069 1.0527037 1.2673616 1.029722
## [16,] 1.0345494 1.0118631 0.9977275 1.052909 1.0195699 1.1513718 1.018715
## [17,] 1.0882658 1.0560688 1.0529093 0.998596 1.1024259 1.0217043 1.110433
## [18,] 1.0844502 1.0527037 1.0195699 1.102426 0.9978386 1.2679677 1.082628
## [19,] 1.1132210 1.2673616 1.1513718 1.021704 1.2679677 0.9979547 1.323612
## [20,] 1.0754465 1.0297215 1.0187147 1.110433 1.0826283 1.3236117 0.998777me20$n.eff## $B
## [,1] [,2] [,3]
## [1,] 114 142 148
## [2,] 39 89 30
## [3,] 21 99 21
## [4,] 23 30 22
## [5,] 26 46 30
## [6,] 88 141 85
## [7,] 39 35 42
## [8,] 675 220 85
## [9,] 1000 265 1000
## [10,] 674 700 218
## [11,] 598 276 1000
## [12,] 180 413 334
## [13,] 438 378 386
## [14,] 366 129 155
## [15,] 56 44 83
## [16,] 53 97 63
## [17,] 23 45 26
## [18,] 42 59 30
## [19,] 19 68 21
## [20,] 37 116 30
##
## $Rho
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
## [1,] 1 181 118 141 73 46 71 89 138 25 49 63 247
## [2,] 181 1 78 200 111 18 147 186 58 40 121 74 37
## [3,] 118 78 1 76 43 28 20 59 124 212 85 91 74
## [4,] 141 200 76 1 354 143 269 122 173 42 66 90 29
## [5,] 73 111 43 354 1 533 128 100 189 370 176 57 42
## [6,] 46 18 28 143 533 1 661 73 175 97 963 78 68
## [7,] 71 147 20 269 128 661 1 212 143 324 100 84 125
## [8,] 89 186 59 122 100 73 212 1 107 395 156 115 115
## [9,] 138 58 124 173 189 175 143 107 1 813 131 143 109
## [10,] 25 40 212 42 370 97 324 395 813 1 106 67 49
## [11,] 49 121 85 66 176 963 100 156 131 106 1 431 244
## [12,] 63 74 91 90 57 78 84 115 143 67 431 1 145
## [13,] 247 37 74 29 42 68 125 115 109 49 244 145 1
## [14,] 567 119 24 413 41 348 26 353 197 135 121 178 318
## [15,] 111 174 59 353 54 31 26 295 42 48 1000 66 75
## [16,] 77 136 59 56 76 47 402 80 44 101 245 450 53
## [17,] 105 82 67 58 70 171 44 80 51 43 57 88 30
## [18,] 316 422 58 81 425 152 110 131 55 164 143 72 30
## [19,] 63 212 130 112 125 110 199 78 56 29 81 45 31
## [20,] 143 364 127 489 821 208 563 80 273 74 318 679 58
## [,14] [,15] [,16] [,17] [,18] [,19] [,20]
## [1,] 567 111 77 105 316 63 143
## [2,] 119 174 136 82 422 212 364
## [3,] 24 59 59 67 58 130 127
## [4,] 413 353 56 58 81 112 489
## [5,] 41 54 76 70 425 125 821
## [6,] 348 31 47 171 152 110 208
## [7,] 26 26 402 44 110 199 563
## [8,] 353 295 80 80 131 78 80
## [9,] 197 42 44 51 55 56 273
## [10,] 135 48 101 43 164 29 74
## [11,] 121 1000 245 57 143 81 318
## [12,] 178 66 450 88 72 45 679
## [13,] 318 75 53 30 30 31 58
## [14,] 1 111 183 58 75 52 86
## [15,] 111 1 221 274 60 333 59
## [16,] 183 221 1 128 70 469 101
## [17,] 58 274 128 1 233 118 159
## [18,] 75 60 70 233 1 117 161
## [19,] 52 333 469 118 117 1 172
## [20,] 86 59 101 159 161 172 1
##
## $EnvRho
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
## [1,] 1 99 33 72 38 483 109 72 139 273 137 356 205
## [2,] 99 1 68 111 59 42 33 28 34 36 43 44 50
## [3,] 33 68 1 51 48 33 47 31 30 32 41 28 39
## [4,] 72 111 51 1 36 51 22 27 40 40 60 49 58
## [5,] 38 59 48 36 1 32 55 57 50 35 66 42 69
## [6,] 483 42 33 51 32 1 58 70 102 233 76 227 133
## [7,] 109 33 47 22 55 58 1 65 61 50 67 62 74
## [8,] 72 28 31 27 57 70 65 1 228 224 195 178 357
## [9,] 139 34 30 40 50 102 61 228 1 351 520 689 1000
## [10,] 273 36 32 40 35 233 50 224 351 1 169 794 489
## [11,] 137 43 41 60 66 76 67 195 520 169 1 310 1000
## [12,] 356 44 28 49 42 227 62 178 689 794 310 1 856
## [13,] 205 50 39 58 69 133 74 357 1000 489 1000 856 1
## [14,] 312 74 32 37 48 149 91 167 351 214 287 456 201
## [15,] 62 38 43 94 58 90 68 152 184 131 103 114 112
## [16,] 148 62 27 315 28 174 39 56 128 121 76 211 132
## [17,] 58 59 56 30 32 90 20 29 36 56 29 47 48
## [18,] 44 34 25 53 64 42 34 52 77 39 123 57 108
## [19,] 100 37 127 42 53 33 50 23 25 29 30 32 31
## [20,] 46 18 15 29 20 89 35 34 56 47 45 73 68
## [,14] [,15] [,16] [,17] [,18] [,19] [,20]
## [1,] 312 62 148 58 44 100 46
## [2,] 74 38 62 59 34 37 18
## [3,] 32 43 27 56 25 127 15
## [4,] 37 94 315 30 53 42 29
## [5,] 48 58 28 32 64 53 20
## [6,] 149 90 174 90 42 33 89
## [7,] 91 68 39 20 34 50 35
## [8,] 167 152 56 29 52 23 34
## [9,] 351 184 128 36 77 25 56
## [10,] 214 131 121 56 39 29 47
## [11,] 287 103 76 29 123 30 45
## [12,] 456 114 211 47 57 32 73
## [13,] 201 112 132 48 108 31 68
## [14,] 1 80 101 40 47 33 48
## [15,] 80 1 1000 63 127 19 172
## [16,] 101 1000 1 96 337 27 188
## [17,] 40 63 96 1 33 355 35
## [18,] 47 127 337 33 1 17 42
## [19,] 33 19 27 355 17 1 18
## [20,] 48 172 188 35 42 18 1me20$mcmc.info[1:7]## $n.chains
## [1] 5
##
## $n.adapt
## [1] 250000 250000 250000 250000 250000
##
## $sufficient.adapt
## [1] FALSE FALSE FALSE FALSE FALSE
##
## $n.iter
## [1] 20000
##
## $n.burnin
## [1] 0
##
## $n.thin
## [1] 100
##
## $n.samples
## [1] 1000models<-list(EnvEvenSp20=me20)
mean_correlations<-mean_cor(models)
x <- subset(mean_correlations, type != "Environmental\nFiltering Only")
acc <- by(x, x$model, function(x) sum(x$status == "TP" | x$status == "TN") / nrow(x))
#' Plot correlation parameter means
#+ plot-correlations
ggplot(mean_correlations) +
aes(factor(nsp), rho, fill = interaction) +
geom_hline(yintercept = 0) +
geom_boxplot(
outlier.size = .2, size = .1, position = position_dodge(preserve = "single")
) +
scale_fill_manual(values = c("grey", "blue", "red")) +
facet_grid(type ~ rho_type + density, switch = "y") +
xlab("Number of species") +
ylab("Correlation") +
theme_bw() +
theme(legend.position = "top")
data<-sim_data$EnvEvenSp20
data <- list(
Y = subset(data, select = -env),
X = cbind(1, scale(poly(data$env, 2))),
covx = cov(cbind(1, scale(poly(data$env, 2)))),
K = 3,
J = ncol(data) - 1,
n = nrow(data),
I = diag(ncol(data) - 1),
df = ncol(data)
)
##########################################################################################
#Tau<-solve
#Tau_n<-to_prec(me10$mean$Tau)
#Tau_k<-Tau_n*(!(model$q97.5$Tau>0 & model$q2.5$Tau<0))
plot_cor_jsdm(me20,data$Y)
Gjam
data<-sim_data$EnvEvenSp20
#fit_gjam(data,5000,500,"./gjam_models/gjam20env.rda")
load_gjam(data,name="./gjam_models/gjam20env.rda")##
## Sensitivity by predictor variables f:
## Estimate SE CI_025 CI_975
## env 335 61.7 222 461
## env2 157 28.3 110 219
##
## Coefficient matrix B:
## sp01 sp02 sp03 sp04 sp05 sp06 sp07 sp08 sp09
## intercept -0.533 0.0688 0.500 0.144 0.327 0.498 0.720 0.896 0.906
## env -1.850 -2.5200 -2.480 -2.020 -1.820 -1.760 -1.220 -0.753 -0.516
## env2 0.187 0.3400 0.451 -0.279 -0.507 -0.493 -0.651 -0.641 -0.815
## RMSPE 0.337 0.3000 0.309 0.331 0.332 0.333 0.365 0.398 0.406
## sp10 sp11 sp12 sp13 sp14 sp15 sp16 sp17 sp18
## intercept 1.130 1.120 1.050 0.689 0.610 0.435 0.440 0.113 -0.07100
## env -0.200 0.133 0.551 0.870 1.140 1.730 2.460 1.990 2.35000
## env2 -0.931 -0.855 -0.860 -0.794 -0.671 -0.452 -0.227 -0.271 0.00271
## RMSPE 0.398 0.411 0.386 0.399 0.363 0.339 0.308 0.330 0.30300
## sp19 sp20
## intercept -0.0697 -0.329
## env 2.5800 2.190
## env2 0.3120 0.530
## RMSPE 0.3030 0.318
##
## Coefficient matrix B:
## Estimate SE CI_025 CI_975 sig95
## sp01_intercept -0.53300 0.1340 -0.764000 -0.3250 *
## sp01_env -1.85000 0.1340 -2.100000 -1.5800 *
## sp01_env2 0.18700 0.0717 0.055600 0.3320 *
## sp02_intercept 0.06880 0.0972 -0.095900 0.2760
## sp02_env -2.52000 0.1560 -2.810000 -2.2200 *
## sp02_env2 0.34000 0.0720 0.206000 0.4840 *
## sp03_intercept 0.50000 0.1040 0.295000 0.6900 *
## sp03_env -2.48000 0.1360 -2.770000 -2.2400 *
## sp03_env2 0.45100 0.0813 0.310000 0.6170 *
## sp04_intercept 0.14400 0.0783 0.009940 0.3210 *
## sp04_env -2.02000 0.0913 -2.200000 -1.8400 *
## sp04_env2 -0.27900 0.0825 -0.432000 -0.1160 *
## sp05_intercept 0.32700 0.0843 0.172000 0.4850 *
## sp05_env -1.82000 0.1010 -2.020000 -1.6200 *
## sp05_env2 -0.50700 0.0716 -0.653000 -0.3720 *
## sp06_intercept 0.49800 0.0786 0.342000 0.6400 *
## sp06_env -1.76000 0.0786 -1.920000 -1.6100 *
## sp06_env2 -0.49300 0.0669 -0.631000 -0.3680 *
## sp07_intercept 0.72000 0.0628 0.598000 0.8430 *
## sp07_env -1.22000 0.0844 -1.370000 -1.0500 *
## sp07_env2 -0.65100 0.0816 -0.819000 -0.4980 *
## sp08_intercept 0.89600 0.0702 0.766000 1.0400 *
## sp08_env -0.75300 0.0678 -0.886000 -0.6230 *
## sp08_env2 -0.64100 0.0614 -0.759000 -0.5180 *
## sp09_intercept 0.90600 0.0712 0.776000 1.0500 *
## sp09_env -0.51600 0.0835 -0.663000 -0.3500 *
## sp09_env2 -0.81500 0.0669 -0.942000 -0.6790 *
## sp10_intercept 1.13000 0.0655 1.000000 1.2600 *
## sp10_env -0.20000 0.0573 -0.305000 -0.0862 *
## sp10_env2 -0.93100 0.0676 -1.060000 -0.8010 *
## sp11_intercept 1.12000 0.0684 0.987000 1.2500 *
## sp11_env 0.13300 0.0565 0.022800 0.2410 *
## sp11_env2 -0.85500 0.0630 -0.978000 -0.7310 *
## sp12_intercept 1.05000 0.0730 0.906000 1.2000 *
## sp12_env 0.55100 0.0747 0.420000 0.7010 *
## sp12_env2 -0.86000 0.0752 -1.010000 -0.7180 *
## sp13_intercept 0.68900 0.0591 0.570000 0.8000 *
## sp13_env 0.87000 0.0813 0.709000 1.0300 *
## sp13_env2 -0.79400 0.0668 -0.922000 -0.6560 *
## sp14_intercept 0.61000 0.0641 0.481000 0.7300 *
## sp14_env 1.14000 0.0675 1.020000 1.2800 *
## sp14_env2 -0.67100 0.0665 -0.800000 -0.5470 *
## sp15_intercept 0.43500 0.0588 0.325000 0.5530 *
## sp15_env 1.73000 0.1250 1.520000 1.9800 *
## sp15_env2 -0.45200 0.0641 -0.579000 -0.3340 *
## sp16_intercept 0.44000 0.0521 0.337000 0.5420 *
## sp16_env 2.46000 0.1050 2.260000 2.6700 *
## sp16_env2 -0.22700 0.0501 -0.323000 -0.1280 *
## sp17_intercept 0.11300 0.0563 0.000618 0.2230 *
## sp17_env 1.99000 0.1310 1.760000 2.2600 *
## sp17_env2 -0.27100 0.0645 -0.394000 -0.1480 *
## sp18_intercept -0.07100 0.0549 -0.181000 0.0357
## sp18_env 2.35000 0.1040 2.140000 2.5500 *
## sp18_env2 0.00271 0.0659 -0.130000 0.1260
## sp19_intercept -0.06970 0.0875 -0.241000 0.0747
## sp19_env 2.58000 0.1220 2.350000 2.8300 *
## sp19_env2 0.31200 0.0517 0.216000 0.4120 *
## sp20_intercept -0.32900 0.0556 -0.433000 -0.2160 *
## sp20_env 2.19000 0.0953 2.010000 2.3800 *
## sp20_env2 0.53000 0.0616 0.409000 0.6490 *
##
## Last column indicates if 95% posterior distribution contains zero.
##
## Coefficient matrix B, standardized for X:
## NULL
##
## Last column indicates if 95% posterior distribution contains zero.
##
## Coefficient matrix B, standardized for X and W:
## NULL
##
## Last column indicates if 95% posterior distribution contains zero.
##
## Design Table
## env env2
## VIF 1 1
## env2 0 NA
##
## Sample contains n = 500 observations on S = 20 response variables, and 2 predictors. Data types (typeNames) include PA. There are 0 missing values in X and 0 missing values in Y. The RMSPE is 0.351, and the DIC is 371075. Computation involved 5000 Gibbs steps, with a burnin of 500.
#gje20<-load_object("./gjam_models/gjam20env.rda")
#to check posterior density of s in Sigma
#gje20<-load_object("./gjam_models/gjam20env.rda")
#plot(density(gje20$chains$sgibbs[,4]))data<-sim_data$EnvEvenSp20
hm_mod<-fit_hmsc(data,"L_d",name="./HMmodels/hm20env.rda" )
hm_conv(hm_mod)
hm_inter(hm_mod)
Environmental filtering + Facilitation dense 5 species
JSDM
mf5 <- load_object("model-2019-04-11-19-35-11.rda")
summary(mf5)## Summary for model '/tmp/RtmpKix4lZ/file40e94e482135'
## Saved parameters: B Rho EnvRho
## MCMC ran in parallel for 29.13 minutes at time 2019-04-11 19:06:03.
##
## For each of 5 chains:
## Adaptation: 250000 iterations (possibly insufficient)
## Burn-in: 0 iterations
## Thin rate: 100 iterations
## Total chain length: 270000 iterations
## Posterior sample size: 200 draws
##
## **WARNING** Rhat values indicate convergence failure.mf5$Rhat## $B
## [,1] [,2] [,3]
## [1,] 1.061476 1.078281 1.059637
## [2,] 1.050432 1.005249 1.036787
## [3,] 1.029274 1.008142 1.034895
## [4,] 1.308137 1.303643 1.281245
## [5,] 1.024599 1.020683 1.020340
##
## $Rho
## [,1] [,2] [,3] [,4] [,5]
## [1,] 0.9977828 1.0807830 1.0748954 1.002567 1.054513
## [2,] 1.0807830 0.9985899 1.0379842 1.021748 1.007209
## [3,] 1.0748954 1.0379842 0.9978763 1.021928 1.029875
## [4,] 1.0025674 1.0217483 1.0219278 0.998774 1.030838
## [5,] 1.0545132 1.0072085 1.0298750 1.030838 NA
##
## $EnvRho
## [,1] [,2] [,3] [,4] [,5]
## [1,] 0.9996754 1.0312709 1.0567699 1.0928409 1.0541784
## [2,] 1.0312709 0.9976507 1.0200860 1.1323921 1.0501329
## [3,] 1.0567699 1.0200860 0.9978892 1.1094268 1.0149056
## [4,] 1.0928409 1.1323921 1.1094268 0.9996634 1.1421120
## [5,] 1.0541784 1.0501329 1.0149056 1.1421120 0.9992363mf5$n.eff## $B
## [,1] [,2] [,3]
## [1,] 61 47 57
## [2,] 75 496 90
## [3,] 117 305 106
## [4,] 15 15 16
## [5,] 137 161 170
##
## $Rho
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1 44 44 678 61
## [2,] 44 1 88 199 334
## [3,] 44 88 1 282 110
## [4,] 678 199 282 1 171
## [5,] 61 334 110 171 1
##
## $EnvRho
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1 102 59 41 63
## [2,] 102 1 156 33 102
## [3,] 59 156 1 35 225
## [4,] 41 33 35 1 34
## [5,] 63 102 225 34 1mf5$mcmc.info[1:7]## $n.chains
## [1] 5
##
## $n.adapt
## [1] 250000 250000 250000 250000 250000
##
## $sufficient.adapt
## [1] FALSE FALSE FALSE FALSE FALSE
##
## $n.iter
## [1] 20000
##
## $n.burnin
## [1] 0
##
## $n.thin
## [1] 100
##
## $n.samples
## [1] 1000models<-list(FacDenseSp5=mf5)
mean_correlations<-mean_cor(models)
x <- subset(mean_correlations, type != "Environmental\nFiltering Only")
acc <- by(x, x$model, function(x) sum(x$status == "TP" | x$status == "TN") / nrow(x))
#' Plot correlation parameter means
#+ plot-correlations
ggplot(mean_correlations) +
aes(factor(nsp), rho, fill = interaction) +
geom_hline(yintercept = 0) +
geom_boxplot(
outlier.size = .2, size = .1, position = position_dodge(preserve = "single")
) +
scale_fill_manual(values = c("grey", "blue", "red")) +
facet_grid(type ~ rho_type + density, switch = "y") +
xlab("Number of species") +
ylab("Correlation") +
theme_bw() +
theme(legend.position = "top")
data<-sim_data$FacDenseSp5
data <- list(
Y = subset(data, select = -env),
X = cbind(1, scale(poly(data$env, 2))),
covx = cov(cbind(1, scale(poly(data$env, 2)))),
K = 3,
J = ncol(data) - 1,
n = nrow(data),
I = diag(ncol(data) - 1),
df = ncol(data)
)
##########################################################################################
#Tau<-solve
#Tau_n<-to_prec(me10$mean$Tau)
#Tau_k<-Tau_n*(!(model$q97.5$Tau>0 & model$q2.5$Tau<0))
plot_cor_jsdm(mf5,data$Y,fac_inter[[4]])
Gjam
data<-sim_data$FacDenseSp5
#fit_gjam(data,5000,500,"./gjam_models/gjam5f.rda",interact=fac_inter[[4]])
load_gjam(data,name="./gjam_models/gjam5f.rda", interact=fac_inter[[4]])##
## Sensitivity by predictor variables f:
## Estimate SE CI_025 CI_975
## env 113.0 61.5 13.9 237
## env2 67.7 31.3 19.6 134
##
## Coefficient matrix B:
## sp01 sp02 sp03 sp04 sp05
## intercept -1.300 1.020 2.990 1.570 -1.840
## env -1.540 -1.640 0.114 2.570 1.760
## env2 0.558 -0.451 -1.120 0.308 0.299
## RMSPE 0.308 0.315 0.201 0.331 0.308
##
## Coefficient matrix B:
## Estimate SE CI_025 CI_975 sig95
## sp01_intercept -1.300 0.0975 -1.4900 -1.110 *
## sp01_env -1.540 0.1450 -1.8300 -1.300 *
## sp01_env2 0.558 0.0909 0.3960 0.749 *
## sp02_intercept 1.020 0.1180 0.8440 1.300 *
## sp02_env -1.640 0.0966 -1.8200 -1.450 *
## sp02_env2 -0.451 0.0802 -0.6250 -0.298 *
## sp03_intercept 2.990 0.1230 2.7500 3.230 *
## sp03_env 0.114 0.0787 -0.0114 0.287
## sp03_env2 -1.120 0.0715 -1.2600 -0.977 *
## sp04_intercept 1.570 0.0908 1.4100 1.770 *
## sp04_env 2.570 0.1370 2.3300 2.870 *
## sp04_env2 0.308 0.0514 0.2070 0.410 *
## sp05_intercept -1.840 0.1750 -2.1700 -1.510 *
## sp05_env 1.760 0.1520 1.4600 2.060 *
## sp05_env2 0.299 0.0823 0.1450 0.448 *
##
## Last column indicates if 95% posterior distribution contains zero.
##
## Coefficient matrix B, standardized for X:
## NULL
##
## Last column indicates if 95% posterior distribution contains zero.
##
## Coefficient matrix B, standardized for X and W:
## NULL
##
## Last column indicates if 95% posterior distribution contains zero.
##
## Design Table
## env env2
## VIF 1 1
## env2 0 NA
##
## Sample contains n = 500 observations on S = 5 response variables, and 2 predictors. Data types (typeNames) include PA. There are 0 missing values in X and 0 missing values in Y. The RMSPE is 0.296, and the DIC is 80408. Computation involved 5000 Gibbs steps, with a burnin of 500.
#gjfd5<-load_object("./gjam_models/gjam5f.rda")
#to check posterior density of s in Sigma
#gjfd5<-load_object("./gjam_models/gjam5f.rda")
#plot(density(gjfd5$chains$sgibbs[,4]))HMSC
data<-sim_data$FacDenseSp5
hm_mod<-fit_hmsc(data,"L_d",name="./HMmodels/hm5fd.rda" )
hm_conv(hm_mod)
hm_inter(hm_mod, interact = fac_inter[[4]])
Environmental filtering + Facilitation dense 10 species
JSDM
## Summary for model '/tmp/RtmpKix4lZ/file40e96d09e31e'
## Saved parameters: B Rho EnvRho
## MCMC ran in parallel for 684.487 minutes at time 2019-04-11 19:35:12.
##
## For each of 5 chains:
## Adaptation: 250000 iterations (possibly insufficient)
## Burn-in: 0 iterations
## Thin rate: 100 iterations
## Total chain length: 270000 iterations
## Posterior sample size: 200 draws
##
## **WARNING** Rhat values indicate convergence failure.## $B
## [,1] [,2] [,3]
## [1,] 1.153482 1.0336151 1.192794
## [2,] 1.133619 1.1411694 1.148770
## [3,] 1.012998 1.0148224 1.007833
## [4,] 1.178132 1.1516477 1.197430
## [5,] 1.002237 0.9999322 1.002150
## [6,] 1.190968 1.2109106 1.129494
## [7,] 1.009794 1.0040941 1.047714
## [8,] 1.007133 1.0052821 1.001169
## [9,] 1.285740 1.2434730 1.228175
## [10,] 1.217415 1.1142643 1.286613
##
## $Rho
## [,1] [,2] [,3] [,4] [,5] [,6] [,7]
## [1,] 0.9988733 1.031197 1.019898 1.0812913 1.0137249 1.0637758 1.0129501
## [2,] 1.0311967 0.998210 1.012831 1.1195132 1.0044466 1.0592694 1.0033491
## [3,] 1.0198982 1.012831 1.000408 1.0126902 1.0020036 1.0024656 1.0622998
## [4,] 1.0812913 1.119513 1.012690 0.9979297 1.0068483 1.0407943 1.0110697
## [5,] 1.0137249 1.004447 1.002004 1.0068483 0.9981162 1.0124882 1.0166292
## [6,] 1.0637758 1.059269 1.002466 1.0407943 1.0124882 0.9978665 1.0831663
## [7,] 1.0129501 1.003349 1.062300 1.0110697 1.0166292 1.0831663 0.9984459
## [8,] 1.0272182 1.013482 1.000281 1.0123064 1.0047824 1.1064121 1.0145070
## [9,] 1.0109590 1.011512 1.022300 1.0414561 1.0053878 1.1066725 1.0033029
## [10,] 1.0369179 1.009054 1.002782 1.0435671 1.0080731 1.0504700 1.0603705
## [,8] [,9] [,10]
## [1,] 1.0272182 1.0109590 1.036918
## [2,] 1.0134820 1.0115116 1.009054
## [3,] 1.0002814 1.0222999 1.002782
## [4,] 1.0123064 1.0414561 1.043567
## [5,] 1.0047824 1.0053878 1.008073
## [6,] 1.1064121 1.1066725 1.050470
## [7,] 1.0145070 1.0033029 1.060371
## [8,] 0.9984815 0.9990499 1.005105
## [9,] 0.9990499 0.9982693 1.019467
## [10,] 1.0051052 1.0194674 NA
##
## $EnvRho
## [,1] [,2] [,3] [,4] [,5] [,6] [,7]
## [1,] 0.9979932 1.0620907 1.1552389 1.126374 1.0815389 1.1780759 1.0943292
## [2,] 1.0620907 0.9976796 1.0557414 1.081622 1.0590928 1.1310287 1.0309328
## [3,] 1.1552389 1.0557414 0.9986019 1.173177 1.0027567 1.1664742 1.0132200
## [4,] 1.1263736 1.0816223 1.1731773 1.000214 1.1261088 1.1220314 1.1087357
## [5,] 1.0815389 1.0590928 1.0027567 1.126109 0.9984384 1.1508615 1.0172239
## [6,] 1.1780759 1.1310287 1.1664742 1.122031 1.1508615 0.9982952 1.0915563
## [7,] 1.0943292 1.0309328 1.0132200 1.108736 1.0172239 1.0915563 0.9984136
## [8,] 1.1299096 1.0742937 0.9992827 1.116689 1.0021633 1.0489712 1.0108285
## [9,] 1.3558782 1.4807811 1.2708682 1.181207 1.1392039 1.2012272 1.2274419
## [10,] 1.3150484 1.4503682 1.2632536 1.196921 1.1060001 1.2696838 1.1913738
## [,8] [,9] [,10]
## [1,] 1.1299096 1.3558782 1.3150484
## [2,] 1.0742937 1.4807811 1.4503682
## [3,] 0.9992827 1.2708682 1.2632536
## [4,] 1.1166894 1.1812072 1.1969212
## [5,] 1.0021633 1.1392039 1.1060001
## [6,] 1.0489712 1.2012272 1.2696838
## [7,] 1.0108285 1.2274419 1.1913738
## [8,] 0.9985681 1.1882731 1.1307389
## [9,] 1.1882731 0.9991252 1.0640158
## [10,] 1.1307389 1.0640158 0.9978327## $B
## [,1] [,2] [,3]
## [1,] 25 94 22
## [2,] 27 26 25
## [3,] 223 198 394
## [4,] 24 27 23
## [5,] 661 1000 896
## [6,] 22 20 29
## [7,] 275 464 65
## [8,] 445 489 889
## [9,] 16 18 19
## [10,] 19 33 16
##
## $Rho
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 1 96 162 47 215 61 204 110 270 93
## [2,] 96 1 200 34 843 55 883 202 223 443
## [3,] 162 200 1 272 1000 801 53 1000 132 757
## [4,] 47 34 272 1 387 126 281 247 83 72
## [5,] 215 843 1000 387 1 233 210 467 590 357
## [6,] 61 55 801 126 233 1 41 33 35 71
## [7,] 204 883 53 281 210 41 1 184 531 55
## [8,] 110 202 1000 247 467 33 184 1 1000 864
## [9,] 270 223 132 83 590 35 531 1000 1 161
## [10,] 93 443 757 72 357 71 55 864 161 1
##
## $EnvRho
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 1 109 29 42 43 25 42 29 15 15
## [2,] 109 1 62 60 54 33 98 44 12 12
## [3,] 29 62 1 26 580 27 200 1000 17 17
## [4,] 42 60 26 1 30 34 37 32 24 22
## [5,] 43 54 580 30 1 26 163 740 27 34
## [6,] 25 33 27 34 26 1 46 88 26 22
## [7,] 42 98 200 37 163 46 1 499 19 20
## [8,] 29 44 1000 32 740 88 499 1 22 27
## [9,] 15 12 17 24 27 26 19 22 1 105
## [10,] 15 12 17 22 34 22 20 27 105 1## $n.chains
## [1] 5
##
## $n.adapt
## [1] 250000 250000 250000 250000 250000
##
## $sufficient.adapt
## [1] FALSE FALSE FALSE FALSE FALSE
##
## $n.iter
## [1] 20000
##
## $n.burnin
## [1] 0
##
## $n.thin
## [1] 100
##
## $n.samples
## [1] 1000
data<-sim_data$FacDenseSp10
#fit_gjam(data,5000,500,"./gjam_models/gjam10fd.rda",interact=fac_inter[[5]])
load_gjam(data,name="./gjam_models/gjam10fd.rda", interact=fac_inter[[5]])##
## Sensitivity by predictor variables f:
## Estimate SE CI_025 CI_975
## env 160.0 35.60 101.0 240
## env2 18.5 4.88 10.4 29
##
## Coefficient matrix B:
## sp01 sp02 sp03 sp04 sp05 sp06 sp07 sp08 sp09
## intercept 0.212 -1.490 -0.877 2.410 -0.0951 2.400 0.497 -0.401 1.260
## env -2.760 -2.660 -1.810 -3.010 -0.5910 1.160 1.330 1.030 3.990
## env2 1.070 -0.969 -0.644 0.319 -0.7020 -0.454 -0.256 -0.347 0.095
## RMSPE 0.274 0.375 0.406 0.305 0.4930 0.259 0.390 0.471 0.248
## sp10
## intercept -0.893
## env 2.250
## env2 -0.411
## RMSPE 0.354
##
## Coefficient matrix B:
## Estimate SE CI_025 CI_975 sig95
## sp01_intercept 0.2120 0.0687 0.0833 0.3520 *
## sp01_env -2.7600 0.1740 -3.1200 -2.4700 *
## sp01_env2 1.0700 0.1350 0.8190 1.3200 *
## sp02_intercept -1.4900 0.2000 -1.9200 -1.1600 *
## sp02_env -2.6600 0.2110 -3.0900 -2.3000 *
## sp02_env2 -0.9690 0.1130 -1.2400 -0.7780 *
## sp03_intercept -0.8770 0.1070 -1.0900 -0.6900 *
## sp03_env -1.8100 0.1470 -2.1200 -1.5700 *
## sp03_env2 -0.6440 0.1190 -0.9250 -0.4570 *
## sp04_intercept 2.4100 0.2040 2.0500 2.8000 *
## sp04_env -3.0100 0.2470 -3.4600 -2.5900 *
## sp04_env2 0.3190 0.0696 0.1860 0.4540 *
## sp05_intercept -0.0951 0.0554 -0.2050 0.0147
## sp05_env -0.5910 0.0628 -0.7140 -0.4700 *
## sp05_env2 -0.7020 0.0743 -0.8490 -0.5590 *
## sp06_intercept 2.4000 0.1260 2.1600 2.6400 *
## sp06_env 1.1600 0.0761 1.0100 1.3100 *
## sp06_env2 -0.4540 0.0649 -0.5840 -0.3310 *
## sp07_intercept 0.4970 0.0563 0.3840 0.6040 *
## sp07_env 1.3300 0.0797 1.1800 1.4900 *
## sp07_env2 -0.2560 0.0554 -0.3670 -0.1470 *
## sp08_intercept -0.4010 0.0539 -0.5070 -0.2950 *
## sp08_env 1.0300 0.0877 0.8540 1.1900 *
## sp08_env2 -0.3470 0.0592 -0.4640 -0.2320 *
## sp09_intercept 1.2600 0.0922 1.0900 1.4500 *
## sp09_env 3.9900 0.3810 3.3800 4.6800 *
## sp09_env2 0.0950 0.0613 -0.0361 0.2050
## sp10_intercept -0.8930 0.0796 -1.0700 -0.7550 *
## sp10_env 2.2500 0.1280 2.0400 2.5200 *
## sp10_env2 -0.4110 0.0576 -0.5230 -0.2980 *
##
## Last column indicates if 95% posterior distribution contains zero.
##
## Coefficient matrix B, standardized for X:
## NULL
##
## Last column indicates if 95% posterior distribution contains zero.
##
## Coefficient matrix B, standardized for X and W:
## NULL
##
## Last column indicates if 95% posterior distribution contains zero.
##
## Design Table
## env env2
## VIF 1 1
## env2 0 NA
##
## Sample contains n = 500 observations on S = 10 response variables, and 2 predictors. Data types (typeNames) include PA. There are 0 missing values in X and 0 missing values in Y. The RMSPE is 0.367, and the DIC is 157914. Computation involved 5000 Gibbs steps, with a burnin of 500.
#gjfd5<-load_object("./gjam_models/gjam10fd.rda")
#to check posterior density of s in Sigma
#gjfd5<-load_object("./gjam_models/gjam10fd.rda")
#plot(density(gjfd5$chains$sgibbs[,4]))HMSC
Environmental filtering + Facilitation dense 20 species
JSDM
## Summary for model '/tmp/RtmpKix4lZ/file40e91ec9ff9'
## Saved parameters: B Rho EnvRho
## MCMC ran in parallel for 2062.467 minutes at time 2019-04-12 06:59:42.
##
## For each of 5 chains:
## Adaptation: 250000 iterations (possibly insufficient)
## Burn-in: 0 iterations
## Thin rate: 100 iterations
## Total chain length: 270000 iterations
## Posterior sample size: 200 draws
##
## **WARNING** Rhat values indicate convergence failure.## $B
## [,1] [,2] [,3]
## [1,] 1.376190 1.0558773 1.417312
## [2,] 1.074703 1.0817636 1.081799
## [3,] 1.011400 1.0171340 1.034544
## [4,] 1.613198 1.1296227 1.554124
## [5,] 1.071209 1.0946053 1.100012
## [6,] 1.115704 1.1124290 1.100328
## [7,] 1.007029 1.0154298 1.030331
## [8,] 1.003984 1.0030036 1.008055
## [9,] 1.025315 1.0164277 1.012605
## [10,] 1.030228 1.0030305 1.010353
## [11,] 1.004568 0.9990085 1.023995
## [12,] 1.003844 1.0144397 1.006972
## [13,] 1.009875 1.0165315 1.021270
## [14,] 1.062406 1.0196660 1.015510
## [15,] 1.028030 1.0127785 1.021736
## [16,] 1.034695 1.0181825 1.054500
## [17,] 1.094448 1.0257206 1.103735
## [18,] 1.091727 1.0828716 1.065255
## [19,] 1.282707 1.1298502 1.330849
## [20,] 1.168245 1.1176696 1.141811
##
## $Rho
## [,1] [,2] [,3] [,4] [,5] [,6] [,7]
## [1,] 0.9982905 1.008771 1.0061375 1.0788395 1.009030 1.095454 1.0216688
## [2,] 1.0087715 1.000078 1.0090774 1.0078921 1.105212 1.044087 1.0202222
## [3,] 1.0061375 1.009077 0.9987511 1.0650208 1.058049 1.033873 1.0162547
## [4,] 1.0788395 1.007892 1.0650208 0.9986979 1.010182 1.037413 1.1031827
## [5,] 1.0090298 1.105212 1.0580490 1.0101820 1.000651 1.012787 1.0197768
## [6,] 1.0954535 1.044087 1.0338730 1.0374131 1.012787 1.000065 1.1114474
## [7,] 1.0216688 1.020222 1.0162547 1.1031827 1.019777 1.111447 0.9984712
## [8,] 1.0421743 1.027521 1.0556269 1.0486871 1.004953 1.039428 1.0091695
## [9,] 1.0491384 1.081933 1.0149809 1.1409019 1.018936 1.025693 1.0197720
## [10,] 1.1607294 1.152438 1.0090012 1.0315944 1.072180 1.009837 1.0226942
## [11,] 1.0169814 1.018941 1.0190009 1.0463823 1.021600 1.077737 1.0310290
## [12,] 1.0288528 1.017582 1.0091793 1.0675219 1.053340 1.068944 1.0162678
## [13,] 1.0171323 1.030387 1.0271916 1.0868156 1.072540 1.018329 1.0495081
## [14,] 1.0116781 1.029884 1.0235713 1.1044660 1.021622 1.016754 1.0454174
## [15,] 1.0294567 1.039518 1.0042398 1.0793017 1.093085 1.068150 1.0240445
## [16,] 1.0050553 1.063294 1.0302294 1.0604339 1.071086 1.051623 1.0097124
## [17,] 1.0076387 1.035886 1.0531829 1.0390297 1.045285 1.006324 1.0176165
## [18,] 1.0263156 1.023363 1.0394359 1.0165522 1.004494 1.021192 1.0133820
## [19,] 1.0257184 1.044273 1.0358410 1.0334129 1.008060 1.045058 1.0217908
## [20,] 1.0174202 1.022821 1.0158514 1.0312164 1.029276 1.010109 1.0375571
## [,8] [,9] [,10] [,11] [,12] [,13] [,14]
## [1,] 1.0421743 1.0491384 1.1607294 1.0169814 1.028853 1.0171323 1.0116781
## [2,] 1.0275208 1.0819326 1.1524381 1.0189412 1.017582 1.0303869 1.0298838
## [3,] 1.0556269 1.0149809 1.0090012 1.0190009 1.009179 1.0271916 1.0235713
## [4,] 1.0486871 1.1409019 1.0315944 1.0463823 1.067522 1.0868156 1.1044660
## [5,] 1.0049532 1.0189364 1.0721797 1.0215999 1.053340 1.0725396 1.0216217
## [6,] 1.0394279 1.0256935 1.0098367 1.0777371 1.068944 1.0183288 1.0167538
## [7,] 1.0091695 1.0197720 1.0226942 1.0310290 1.016268 1.0495081 1.0454174
## [8,] 0.9976755 1.0237740 1.0305832 1.0078299 1.010643 1.0649194 1.0774921
## [9,] 1.0237740 0.9978824 1.0119416 1.0114924 1.023305 1.0585167 1.0965350
## [10,] 1.0305832 1.0119416 0.9977778 1.0107151 1.007352 1.0154822 1.0262685
## [11,] 1.0078299 1.0114924 1.0107151 0.9985406 1.008055 1.0126642 1.0329052
## [12,] 1.0106433 1.0233055 1.0073519 1.0080548 0.998600 1.0038325 1.0153099
## [13,] 1.0649194 1.0585167 1.0154822 1.0126642 1.003832 0.9984417 1.0176475
## [14,] 1.0774921 1.0965350 1.0262685 1.0329052 1.015310 1.0176475 0.9982314
## [15,] 1.0264074 1.0446357 1.0791844 1.0094512 1.020979 1.0383925 1.0405234
## [16,] 1.0555903 1.1842121 1.1397342 1.0216436 1.006298 1.0972664 1.0329415
## [17,] 1.0300742 1.0740353 1.0692992 1.0268783 1.033863 1.1247628 1.0251688
## [18,] 1.0156435 1.0129000 1.0627640 1.1342464 1.003133 1.0143853 1.0291242
## [19,] 1.0901816 1.1057923 1.0182179 1.0905179 1.064108 1.1310804 1.0205923
## [20,] 1.0233796 1.0089226 1.0785589 1.0240788 1.049107 1.0930434 1.0830906
## [,15] [,16] [,17] [,18] [,19] [,20]
## [1,] 1.029457 1.0050553 1.0076387 1.0263156 1.0257184 1.017420
## [2,] 1.039518 1.0632937 1.0358859 1.0233629 1.0442731 1.022821
## [3,] 1.004240 1.0302294 1.0531829 1.0394359 1.0358410 1.015851
## [4,] 1.079302 1.0604339 1.0390297 1.0165522 1.0334129 1.031216
## [5,] 1.093085 1.0710857 1.0452854 1.0044938 1.0080603 1.029276
## [6,] 1.068150 1.0516226 1.0063242 1.0211920 1.0450582 1.010109
## [7,] 1.024045 1.0097124 1.0176165 1.0133820 1.0217908 1.037557
## [8,] 1.026407 1.0555903 1.0300742 1.0156435 1.0901816 1.023380
## [9,] 1.044636 1.1842121 1.0740353 1.0129000 1.1057923 1.008923
## [10,] 1.079184 1.1397342 1.0692992 1.0627640 1.0182179 1.078559
## [11,] 1.009451 1.0216436 1.0268783 1.1342464 1.0905179 1.024079
## [12,] 1.020979 1.0062975 1.0338630 1.0031331 1.0641080 1.049107
## [13,] 1.038392 1.0972664 1.1247628 1.0143853 1.1310804 1.093043
## [14,] 1.040523 1.0329415 1.0251688 1.0291242 1.0205923 1.083091
## [15,] 1.000352 1.0235770 1.0257166 1.0409805 1.1325189 1.014841
## [16,] 1.023577 0.9989292 1.0153353 1.0267468 1.0378189 1.018608
## [17,] 1.025717 1.0153353 0.9990633 1.0227679 1.0201524 1.012318
## [18,] 1.040981 1.0267468 1.0227679 0.9982089 1.0395519 1.015322
## [19,] 1.132519 1.0378189 1.0201524 1.0395519 0.9980641 1.058135
## [20,] 1.014841 1.0186084 1.0123176 1.0153218 1.0581347 NA
##
## $EnvRho
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 0.9981952 1.2962334 1.2590747 1.2779156 1.2378309 1.3021830
## [2,] 1.2962334 0.9984844 1.0372082 1.4508535 1.0840534 1.1642475
## [3,] 1.2590747 1.0372082 0.9979626 1.2971322 1.0378016 1.0725820
## [4,] 1.2779156 1.4508535 1.2971322 0.9982414 1.5080367 1.2888365
## [5,] 1.2378309 1.0840534 1.0378016 1.5080367 0.9982248 1.1256293
## [6,] 1.3021830 1.1642475 1.0725820 1.2888365 1.1256293 0.9977014
## [7,] 1.3350597 1.0791270 1.0259420 1.4350985 1.0567047 1.0763377
## [8,] 1.3081898 1.0510011 1.0232430 1.4176703 1.0414941 1.0942146
## [9,] 1.2952902 1.0420498 1.0115281 1.4122190 1.0220795 1.0963735
## [10,] 1.2557836 1.0206197 1.0325317 1.4063176 1.0193963 1.1189370
## [11,] 1.2336963 1.0291512 1.0095906 1.3139512 1.0142752 1.0727224
## [12,] 1.2471062 1.0219431 1.0267858 1.3598573 1.0313201 1.1077870
## [13,] 1.2951416 1.0434651 1.0229407 1.4005936 1.0284676 1.1046606
## [14,] 1.2892076 1.0425613 1.0404888 1.3995243 1.0239503 1.1016006
## [15,] 1.3173507 1.0463249 1.0206049 1.4129646 1.0374952 1.1162183
## [16,] 1.3065375 1.1447487 1.0209884 1.2939394 1.0860556 1.0349452
## [17,] 1.0642736 1.0370507 1.0452255 1.2032613 1.0413651 1.1090956
## [18,] 1.2265997 1.0945057 1.0404913 1.3383348 1.0987603 1.0791234
## [19,] 1.1659138 1.1965960 1.2699753 1.1224340 1.1966557 1.1954237
## [20,] 1.2164226 1.0768137 1.0783577 1.3002161 1.0899083 1.2215159
## [,7] [,8] [,9] [,10] [,11] [,12]
## [1,] 1.3350597 1.3081898 1.2952902 1.2557836 1.2336963 1.2471062
## [2,] 1.0791270 1.0510011 1.0420498 1.0206197 1.0291512 1.0219431
## [3,] 1.0259420 1.0232430 1.0115281 1.0325317 1.0095906 1.0267858
## [4,] 1.4350985 1.4176703 1.4122190 1.4063176 1.3139512 1.3598573
## [5,] 1.0567047 1.0414941 1.0220795 1.0193963 1.0142752 1.0313201
## [6,] 1.0763377 1.0942146 1.0963735 1.1189370 1.0727224 1.1077870
## [7,] 0.9978644 1.0082685 1.0164318 1.0132323 1.0016842 1.0105357
## [8,] 1.0082685 0.9980743 1.0021699 1.0068113 0.9982225 1.0056340
## [9,] 1.0164318 1.0021699 0.9981867 1.0094352 0.9993050 1.0061733
## [10,] 1.0132323 1.0068113 1.0094352 0.9978899 1.0034264 1.0008559
## [11,] 1.0016842 0.9982225 0.9993050 1.0034264 0.9985240 1.0019659
## [12,] 1.0105357 1.0056340 1.0061733 1.0008559 1.0019659 0.9979893
## [13,] 1.0178895 1.0100388 1.0006110 1.0067462 1.0015960 1.0061658
## [14,] 1.0074721 1.0039539 1.0103291 1.0046501 1.0037454 1.0109154
## [15,] 1.0232224 1.0111400 1.0012612 1.0122250 1.0044454 1.0063018
## [16,] 1.0374900 1.0444998 1.0403165 1.0612613 1.0251882 1.0571402
## [17,] 1.1080038 1.0887064 1.0542962 1.0643649 1.0657425 1.0645492
## [18,] 1.0168118 1.0231071 1.0289775 1.0238655 1.0157642 1.0206559
## [19,] 1.2624819 1.2615101 1.2275395 1.1811468 1.1792001 1.1674168
## [20,] 1.1441809 1.1222947 1.1087246 1.0938808 1.0938538 1.1127080
## [,13] [,14] [,15] [,16] [,17] [,18]
## [1,] 1.2951416 1.2892076 1.3173507 1.3065375 1.0642736 1.2265997
## [2,] 1.0434651 1.0425613 1.0463249 1.1447487 1.0370507 1.0945057
## [3,] 1.0229407 1.0404888 1.0206049 1.0209884 1.0452255 1.0404913
## [4,] 1.4005936 1.3995243 1.4129646 1.2939394 1.2032613 1.3383348
## [5,] 1.0284676 1.0239503 1.0374952 1.0860556 1.0413651 1.0987603
## [6,] 1.1046606 1.1016006 1.1162183 1.0349452 1.1090956 1.0791234
## [7,] 1.0178895 1.0074721 1.0232224 1.0374900 1.1080038 1.0168118
## [8,] 1.0100388 1.0039539 1.0111400 1.0444998 1.0887064 1.0231071
## [9,] 1.0006110 1.0103291 1.0012612 1.0403165 1.0542962 1.0289775
## [10,] 1.0067462 1.0046501 1.0122250 1.0612613 1.0643649 1.0238655
## [11,] 1.0015960 1.0037454 1.0044454 1.0251882 1.0657425 1.0157642
## [12,] 1.0061658 1.0109154 1.0063018 1.0571402 1.0645492 1.0206559
## [13,] 0.9987243 1.0084284 1.0005957 1.0399168 1.0839167 1.0351159
## [14,] 1.0084284 0.9978177 1.0145308 1.0584693 1.0978121 1.0392557
## [15,] 1.0005957 1.0145308 0.9981296 1.0507099 1.0809332 1.0317971
## [16,] 1.0399168 1.0584693 1.0507099 0.9987887 1.1996512 1.0346758
## [17,] 1.0839167 1.0978121 1.0809332 1.1996512 0.9978612 1.1058675
## [18,] 1.0351159 1.0392557 1.0317971 1.0346758 1.1058675 0.9981475
## [19,] 1.2385736 1.1963221 1.2338101 1.3474897 1.0857633 1.1838293
## [20,] 1.1313576 1.1321472 1.1332496 1.1639659 1.0402368 1.2256711
## [,19] [,20]
## [1,] 1.1659138 1.2164226
## [2,] 1.1965960 1.0768137
## [3,] 1.2699753 1.0783577
## [4,] 1.1224340 1.3002161
## [5,] 1.1966557 1.0899083
## [6,] 1.1954237 1.2215159
## [7,] 1.2624819 1.1441809
## [8,] 1.2615101 1.1222947
## [9,] 1.2275395 1.1087246
## [10,] 1.1811468 1.0938808
## [11,] 1.1792001 1.0938538
## [12,] 1.1674168 1.1127080
## [13,] 1.2385736 1.1313576
## [14,] 1.1963221 1.1321472
## [15,] 1.2338101 1.1332496
## [16,] 1.3474897 1.1639659
## [17,] 1.0857633 1.0402368
## [18,] 1.1838293 1.2256711
## [19,] 0.9980883 1.1118400
## [20,] 1.1118400 0.9977901## $B
## [,1] [,2] [,3]
## [1,] 13 57 12
## [2,] 46 42 42
## [3,] 301 176 90
## [4,] 10 29 10
## [5,] 46 37 35
## [6,] 34 32 37
## [7,] 536 181 102
## [8,] 868 1000 388
## [9,] 121 172 238
## [10,] 100 625 303
## [11,] 499 1000 121
## [12,] 1000 184 395
## [13,] 269 166 153
## [14,] 54 149 179
## [15,] 112 231 138
## [16,] 108 227 63
## [17,] 42 117 37
## [18,] 44 43 71
## [19,] 16 29 14
## [20,] 23 31 27
##
## $Rho
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
## [1,] 1 353 436 46 381 42 169 83 93 24 171 130 160
## [2,] 353 1 392 339 33 80 150 134 41 26 184 175 106
## [3,] 436 392 1 51 59 101 171 59 297 569 166 359 111
## [4,] 46 339 51 1 1000 90 35 68 27 118 78 50 40
## [5,] 381 33 59 1000 1 269 156 867 283 48 130 61 46
## [6,] 42 80 101 90 269 1 33 79 162 336 44 51 220
## [7,] 169 150 171 35 156 33 1 303 143 127 97 190 66
## [8,] 83 134 59 68 867 79 303 1 147 109 472 344 52
## [9,] 93 41 297 27 283 162 143 147 1 292 314 137 62
## [10,] 24 26 569 118 48 336 127 109 292 1 367 362 180
## [11,] 171 184 166 78 130 44 97 472 314 367 1 326 229
## [12,] 130 175 359 50 61 51 190 344 137 362 326 1 1000
## [13,] 160 106 111 40 46 220 66 52 62 180 229 1000 1
## [14,] 243 134 169 37 160 166 73 44 36 131 100 182 173
## [15,] 113 82 622 46 37 51 136 145 72 46 273 159 85
## [16,] 555 52 111 58 47 64 285 64 22 28 151 525 36
## [17,] 360 84 62 79 73 569 191 106 45 52 120 102 29
## [18,] 129 128 86 191 621 197 226 202 257 53 32 1000 817
## [19,] 119 73 102 119 348 70 168 41 35 163 41 52 29
## [20,] 190 194 199 117 127 376 84 122 674 46 124 70 40
## [,14] [,15] [,16] [,17] [,18] [,19] [,20]
## [1,] 243 113 555 360 129 119 190
## [2,] 134 82 52 84 128 73 194
## [3,] 169 622 111 62 86 102 199
## [4,] 37 46 58 79 191 119 117
## [5,] 160 37 47 73 621 348 127
## [6,] 166 51 64 569 197 70 376
## [7,] 73 136 285 191 226 168 84
## [8,] 44 145 64 106 202 41 122
## [9,] 36 72 22 45 257 35 674
## [10,] 131 46 28 52 53 163 46
## [11,] 100 273 151 120 32 41 124
## [12,] 182 159 525 102 1000 52 70
## [13,] 173 85 36 29 817 29 40
## [14,] 1 90 96 127 116 154 43
## [15,] 90 1 127 137 82 28 237
## [16,] 96 127 1 279 122 95 192
## [17,] 127 137 279 1 136 151 227
## [18,] 116 82 122 136 1 90 236
## [19,] 154 28 95 151 90 1 57
## [20,] 43 237 192 227 236 57 1
##
## $EnvRho
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
## [1,] 1 16 18 17 18 15 14 15 15 17 18 17 15
## [2,] 16 1 105 12 62 36 45 71 75 190 103 139 74
## [3,] 18 105 1 17 87 57 112 130 220 93 266 109 139
## [4,] 17 12 17 1 11 17 12 12 12 12 15 13 12
## [5,] 18 62 87 11 1 43 62 92 135 162 193 102 124
## [6,] 15 36 57 17 43 1 48 39 39 32 47 33 35
## [7,] 14 45 112 12 62 48 1 551 219 209 874 259 208
## [8,] 15 71 130 12 92 39 551 1 755 425 1000 409 416
## [9,] 15 75 220 12 135 39 219 755 1 407 1000 501 1000
## [10,] 17 190 93 12 162 32 209 425 407 1 668 1000 340
## [11,] 18 103 266 15 193 47 874 1000 1000 668 1 708 1000
## [12,] 17 139 109 13 102 33 259 409 501 1000 708 1 554
## [13,] 15 74 139 12 124 35 208 416 1000 340 1000 554 1
## [14,] 15 78 76 12 133 36 314 516 264 557 591 322 306
## [15,] 14 72 147 12 83 32 133 231 1000 222 611 434 1000
## [16,] 15 26 138 15 40 93 84 70 78 53 119 59 78
## [17,] 80 89 71 23 76 41 35 42 64 55 57 57 50
## [18,] 19 44 92 16 39 44 230 151 136 127 223 241 132
## [19,] 32 22 17 38 21 23 17 17 18 22 22 23 18
## [20,] 21 78 48 17 40 21 27 31 34 39 38 34 30
## [,14] [,15] [,16] [,17] [,18] [,19] [,20]
## [1,] 15 14 15 80 19 32 21
## [2,] 78 72 26 89 44 22 78
## [3,] 76 147 138 71 92 17 48
## [4,] 12 12 15 23 16 38 17
## [5,] 133 83 40 76 39 21 40
## [6,] 36 32 93 41 44 23 21
## [7,] 314 133 84 35 230 17 27
## [8,] 516 231 70 42 151 17 31
## [9,] 264 1000 78 64 136 18 34
## [10,] 557 222 53 55 127 22 39
## [11,] 591 611 119 57 223 22 38
## [12,] 322 434 59 57 241 23 34
## [13,] 306 1000 78 50 132 18 30
## [14,] 1 248 61 40 106 21 29
## [15,] 248 1 63 54 124 18 30
## [16,] 61 63 1 29 124 14 24
## [17,] 40 54 29 1 49 59 112
## [18,] 106 124 124 49 1 23 25
## [19,] 21 18 14 59 23 1 42
## [20,] 29 30 24 112 25 42 1## $n.chains
## [1] 5
##
## $n.adapt
## [1] 250000 250000 250000 250000 250000
##
## $sufficient.adapt
## [1] FALSE FALSE FALSE FALSE FALSE
##
## $n.iter
## [1] 20000
##
## $n.burnin
## [1] 0
##
## $n.thin
## [1] 100
##
## $n.samples
## [1] 1000
Gjam
data<-sim_data$FacDenseSp20
fit_gjam(data,5000,500,"./gjam_models/gjam20fd.rda",interact=fac_inter[[6]])##
## Observations and responses:
## [1] 500 20
## ===========================================================================## expanding covariance chains## ===========================================================================
## Sensitivity by predictor variables f:
## Estimate SE CI_025 CI_975
## env 257 47.5 168.0 360
## env2 139 25.5 96.1 195
##
## Coefficient matrix B:
## sp01 sp02 sp03 sp04 sp05 sp06 sp07 sp08
## intercept -0.627 -0.7840 -0.782 0.3420 -0.0863 0.571 0.0562 0.308
## env -1.960 -1.7000 -1.960 -2.3400 -1.3300 -2.370 -0.9180 -0.736
## env2 0.347 -0.0633 -0.457 -0.0897 -0.4940 -0.705 -0.6180 -0.926
## RMSPE 0.320 0.3650 0.372 0.3070 0.4090 0.279 0.4540 0.441
## sp09 sp10 sp11 sp12 sp13 sp14 sp15 sp16 sp17
## intercept 0.607 0.589 0.2400 0.301 0.554 0.173 -0.119 0.0146 0.1620
## env -0.747 -0.398 0.0591 0.458 0.974 1.150 1.590 2.0000 2.0000
## env2 -1.000 -1.040 -0.8710 -0.891 -0.920 -0.968 -1.090 -0.4860 0.0644
## RMSPE 0.397 0.421 0.4730 0.455 0.393 0.403 0.371 0.3310 0.3310
## sp18 sp19 sp20
## intercept 0.349 -0.333 -0.606
## env 2.670 3.300 2.070
## env2 0.470 0.323 0.173
## RMSPE 0.291 0.272 0.319
##
## Coefficient matrix B:
## Estimate SE CI_025 CI_975 sig95
## sp01_intercept -0.6270 0.0813 -0.8090 -0.4850 *
## sp01_env -1.9600 0.1460 -2.2300 -1.6900 *
## sp01_env2 0.3470 0.0850 0.1690 0.5020 *
## sp02_intercept -0.7840 0.0631 -0.9040 -0.6620 *
## sp02_env -1.7000 0.1090 -1.9500 -1.5100 *
## sp02_env2 -0.0633 0.0932 -0.2190 0.1080
## sp03_intercept -0.7820 0.0846 -0.9390 -0.6110 *
## sp03_env -1.9600 0.1100 -2.1800 -1.7500 *
## sp03_env2 -0.4570 0.0791 -0.6170 -0.3030 *
## sp04_intercept 0.3420 0.0682 0.1920 0.4640 *
## sp04_env -2.3400 0.1110 -2.5600 -2.1300 *
## sp04_env2 -0.0897 0.0584 -0.2060 0.0164
## sp05_intercept -0.0863 0.0565 -0.1960 0.0218
## sp05_env -1.3300 0.1210 -1.5800 -1.1300 *
## sp05_env2 -0.4940 0.0599 -0.6080 -0.3720 *
## sp06_intercept 0.5710 0.1040 0.3530 0.7310 *
## sp06_env -2.3700 0.1260 -2.6200 -2.1300 *
## sp06_env2 -0.7050 0.0662 -0.8320 -0.5770 *
## sp07_intercept 0.0562 0.0508 -0.0464 0.1520
## sp07_env -0.9180 0.0628 -1.0400 -0.7970 *
## sp07_env2 -0.6180 0.0604 -0.7340 -0.4970 *
## sp08_intercept 0.3080 0.0531 0.2030 0.4100 *
## sp08_env -0.7360 0.0594 -0.8520 -0.6210 *
## sp08_env2 -0.9260 0.0722 -1.0600 -0.7860 *
## sp09_intercept 0.6070 0.0727 0.4740 0.7520 *
## sp09_env -0.7470 0.0690 -0.8800 -0.6130 *
## sp09_env2 -1.0000 0.0719 -1.1500 -0.8690 *
## sp10_intercept 0.5890 0.0623 0.4700 0.7140 *
## sp10_env -0.3980 0.0687 -0.5250 -0.2570 *
## sp10_env2 -1.0400 0.0706 -1.1800 -0.8970 *
## sp11_intercept 0.2400 0.0655 0.1200 0.3700 *
## sp11_env 0.0591 0.0559 -0.0526 0.1640
## sp11_env2 -0.8710 0.0697 -1.0100 -0.7420 *
## sp12_intercept 0.3010 0.0610 0.1800 0.4170 *
## sp12_env 0.4580 0.0569 0.3480 0.5670 *
## sp12_env2 -0.8910 0.0612 -1.0100 -0.7740 *
## sp13_intercept 0.5540 0.0558 0.4470 0.6640 *
## sp13_env 0.9740 0.0637 0.8430 1.1000 *
## sp13_env2 -0.9200 0.0686 -1.0600 -0.7860 *
## sp14_intercept 0.1730 0.0695 0.0500 0.3110 *
## sp14_env 1.1500 0.0620 1.0200 1.2700 *
## sp14_env2 -0.9680 0.0743 -1.1100 -0.8190 *
## sp15_intercept -0.1190 0.0486 -0.2130 -0.0248 *
## sp15_env 1.5900 0.0940 1.4200 1.7800 *
## sp15_env2 -1.0900 0.0932 -1.2700 -0.9210 *
## sp16_intercept 0.0146 0.0713 -0.1210 0.1470
## sp16_env 2.0000 0.0946 1.8100 2.1800 *
## sp16_env2 -0.4860 0.0622 -0.6020 -0.3550 *
## sp17_intercept 0.1620 0.0643 0.0376 0.2850 *
## sp17_env 2.0000 0.0887 1.8300 2.1800 *
## sp17_env2 0.0644 0.0721 -0.0886 0.1920
## sp18_intercept 0.3490 0.0557 0.2430 0.4600 *
## sp18_env 2.6700 0.1210 2.4400 2.9100 *
## sp18_env2 0.4700 0.0606 0.3460 0.5830 *
## sp19_intercept -0.3330 0.0688 -0.4680 -0.2050 *
## sp19_env 3.3000 0.1670 2.9600 3.6200 *
## sp19_env2 0.3230 0.0570 0.2090 0.4340 *
## sp20_intercept -0.6060 0.0716 -0.7450 -0.4740 *
## sp20_env 2.0700 0.1100 1.8700 2.3000 *
## sp20_env2 0.1730 0.0501 0.0734 0.2710 *
##
## Last column indicates if 95% posterior distribution contains zero.
##
## Coefficient matrix B, standardized for X:
## NULL
##
## Last column indicates if 95% posterior distribution contains zero.
##
## Coefficient matrix B, standardized for X and W:
## NULL
##
## Last column indicates if 95% posterior distribution contains zero.
##
## Design Table
## env env2
## VIF 1 1
## env2 0 NA
##
## Sample contains n = 500 observations on S = 20 response variables, and 2 predictors. Data types (typeNames) include PA. There are 0 missing values in X and 0 missing values in Y. The RMSPE is 0.375, and the DIC is 384544. Computation involved 5000 Gibbs steps, with a burnin of 500.
#load_gjam(data,name="./gjam_models/gjam20fd.rda", interact=fac_inter[[6]])
#gjfd5<-load_object("./gjam_models/gjam20fd.rda")
#to check posterior density of s in Sigma
#gjfd5<-load_object("./gjam_models/gjam20fd.rda")
#plot(density(gjfd5$chains$sgibbs[,4]))HMSC
data<-sim_data$FacDenseSp20
hm_mod<-fit_hmsc(data,"F_t",name="./HMmodels/hm20fd.rda" )## [1] "Computing chain 1"
## [1] "Computing chain 2"hm_conv(hm_mod)
hm_inter(hm_mod, interact = fac_inter[[6]])
Environmental filtering + Facilitation sparse 5 species
JSDM
## Summary for model '/tmp/RtmpKix4lZ/file40e917d13dae'
## Saved parameters: B Rho EnvRho
## MCMC ran in parallel for 29.05 minutes at time 2019-04-13 17:22:13.
##
## For each of 5 chains:
## Adaptation: 250000 iterations (possibly insufficient)
## Burn-in: 0 iterations
## Thin rate: 100 iterations
## Total chain length: 270000 iterations
## Posterior sample size: 200 draws
##
## **WARNING** Rhat values indicate convergence failure.## $B
## [,1] [,2] [,3]
## [1,] 1.255418 1.081860 1.241431
## [2,] 1.025291 1.006684 1.026204
## [3,] 1.000823 1.001345 1.004635
## [4,] 1.211812 1.156605 1.209113
## [5,] 1.372985 1.050443 1.354270
##
## $Rho
## [,1] [,2] [,3] [,4] [,5]
## [1,] 0.9977941 1.0261430 1.0323888 1.0242354 1.001623
## [2,] 1.0261430 0.9978558 1.0094400 1.0882210 1.025190
## [3,] 1.0323888 1.0094400 0.9987781 1.0129030 1.160663
## [4,] 1.0242354 1.0882210 1.0129030 0.9978102 1.031993
## [5,] 1.0016226 1.0251898 1.1606628 1.0319930 NA
##
## $EnvRho
## [,1] [,2] [,3] [,4] [,5]
## [1,] 0.9979837 1.2006222 1.213731 1.2027486 1.404787
## [2,] 1.2006222 0.9982306 1.012141 1.2146691 1.238446
## [3,] 1.2137308 1.0121414 0.998331 1.1936308 1.259078
## [4,] 1.2027486 1.2146691 1.193631 0.9990681 1.115127
## [5,] 1.4047869 1.2384459 1.259078 1.1151275 0.997639## $B
## [,1] [,2] [,3]
## [1,] 17 44 17
## [2,] 169 469 127
## [3,] 1000 1000 500
## [4,] 20 25 20
## [5,] 13 66 14
##
## $Rho
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1 111 92 123 819
## [2,] 111 1 283 40 143
## [3,] 92 283 1 205 28
## [4,] 123 40 205 1 102
## [5,] 819 143 28 102 1
##
## $EnvRho
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1 20 19 29 13
## [2,] 20 1 252 20 18
## [3,] 19 252 1 21 17
## [4,] 29 20 21 1 32
## [5,] 13 18 17 32 1## $n.chains
## [1] 5
##
## $n.adapt
## [1] 250000 250000 250000 250000 250000
##
## $sufficient.adapt
## [1] FALSE FALSE FALSE FALSE FALSE
##
## $n.iter
## [1] 20000
##
## $n.burnin
## [1] 0
##
## $n.thin
## [1] 100
##
## $n.samples
## [1] 1000
Environmental filtering + Facilitation sparse 10 species
JSDM
## Summary for model '/tmp/RtmpKix4lZ/file40e9113ddc9'
## Saved parameters: B Rho EnvRho
## MCMC ran in parallel for 686.687 minutes at time 2019-04-13 17:51:16.
##
## For each of 5 chains:
## Adaptation: 250000 iterations (possibly insufficient)
## Burn-in: 0 iterations
## Thin rate: 100 iterations
## Total chain length: 270000 iterations
## Posterior sample size: 200 draws
##
## **WARNING** Rhat values indicate convergence failure.## $B
## [,1] [,2] [,3]
## [1,] 1.079127 1.082576 1.069292
## [2,] 1.493281 1.265593 1.547860
## [3,] 1.228773 1.203569 1.171271
## [4,] 1.001348 1.005780 1.010461
## [5,] 1.000726 1.003454 1.007422
## [6,] 1.009741 1.009018 1.010434
## [7,] 1.001193 1.003615 1.000846
## [8,] 1.055412 1.019015 1.064282
## [9,] 1.007253 1.009355 1.008943
## [10,] 1.082071 1.013491 1.083509
##
## $Rho
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 0.9977516 1.0259531 1.0340329 1.0068896 1.0082188 1.0100219
## [2,] 1.0259531 0.9994816 1.0084724 1.0240600 1.0077530 1.0250177
## [3,] 1.0340329 1.0084724 0.9990054 1.0388638 1.0084130 1.0047410
## [4,] 1.0068896 1.0240600 1.0388638 0.9998256 1.0171348 1.0086691
## [5,] 1.0082188 1.0077530 1.0084130 1.0171348 0.9980083 1.0112323
## [6,] 1.0100219 1.0250177 1.0047410 1.0086691 1.0112323 0.9977598
## [7,] 1.0260664 1.0404806 1.0055234 1.0140496 1.0296225 1.0047627
## [8,] 1.0210249 1.0100324 1.0119074 1.0123030 1.0101896 1.0161500
## [9,] 1.0106588 1.0210052 1.0171462 1.0057247 1.0147989 1.0055479
## [10,] 1.0100571 1.0211629 1.0336511 1.0046977 1.0284525 1.0411663
## [,7] [,8] [,9] [,10]
## [1,] 1.0260664 1.0210249 1.0106588 1.010057
## [2,] 1.0404806 1.0100324 1.0210052 1.021163
## [3,] 1.0055234 1.0119074 1.0171462 1.033651
## [4,] 1.0140496 1.0123030 1.0057247 1.004698
## [5,] 1.0296225 1.0101896 1.0147989 1.028453
## [6,] 1.0047627 1.0161500 1.0055479 1.041166
## [7,] 0.9992092 1.0156770 1.0207471 1.065461
## [8,] 1.0156770 0.9976269 1.0032951 1.018329
## [9,] 1.0207471 1.0032951 0.9978253 1.014369
## [10,] 1.0654609 1.0183288 1.0143692 NA
##
## $EnvRho
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 0.9979914 1.2997558 1.1178625 1.0205384 1.0294759 1.0230069
## [2,] 1.2997558 0.9979005 1.1183563 1.3605378 1.3368724 1.3126681
## [3,] 1.1178625 1.1183563 0.9988495 1.1493055 1.1571554 1.1309736
## [4,] 1.0205384 1.3605378 1.1493055 0.9976748 1.0048199 1.0069981
## [5,] 1.0294759 1.3368724 1.1571554 1.0048199 0.9982209 1.0053027
## [6,] 1.0230069 1.3126681 1.1309736 1.0069981 1.0053027 0.9986515
## [7,] 1.0220474 1.4070747 1.1580578 1.0015551 1.0074252 1.0026820
## [8,] 1.0676153 1.2142164 1.1910105 1.0581631 1.0540330 1.0500890
## [9,] 1.0263475 1.4745656 1.1763028 1.0010915 1.0041818 1.0062623
## [10,] 1.1216597 1.3088215 1.0688491 1.0791185 1.0488323 1.0733602
## [,7] [,8] [,9] [,10]
## [1,] 1.0220474 1.0676153 1.0263475 1.1216597
## [2,] 1.4070747 1.2142164 1.4745656 1.3088215
## [3,] 1.1580578 1.1910105 1.1763028 1.0688491
## [4,] 1.0015551 1.0581631 1.0010915 1.0791185
## [5,] 1.0074252 1.0540330 1.0041818 1.0488323
## [6,] 1.0026820 1.0500890 1.0062623 1.0733602
## [7,] 0.9979547 1.0471104 1.0055009 1.0790593
## [8,] 1.0471104 0.9991044 1.0409568 1.2608559
## [9,] 1.0055009 1.0409568 0.9983401 1.0519840
## [10,] 1.0790593 1.2608559 1.0519840 0.9976922## $B
## [,1] [,2] [,3]
## [1,] 52 49 55
## [2,] 11 17 10
## [3,] 19 20 24
## [4,] 1000 374 266
## [5,] 1000 567 333
## [6,] 262 286 296
## [7,] 839 602 1000
## [8,] 129 212 94
## [9,] 462 346 407
## [10,] 49 251 47
##
## $Rho
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 1 122 94 384 296 316 114 140 292 337
## [2,] 122 1 558 143 481 155 81 262 144 148
## [3,] 94 558 1 89 355 800 1000 317 214 104
## [4,] 384 143 89 1 172 290 191 213 543 438
## [5,] 296 481 355 172 1 273 110 421 192 116
## [6,] 316 155 800 290 273 1 621 178 401 101
## [7,] 114 81 1000 191 110 621 1 179 140 53
## [8,] 140 262 317 213 421 178 179 1 630 181
## [9,] 292 144 214 543 192 401 140 630 1 213
## [10,] 337 148 104 438 116 101 53 181 213 1
##
## $EnvRho
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 1 15 31 147 111 128 138 80 119 41
## [2,] 15 1 37 13 14 15 12 22 11 15
## [3,] 31 37 1 26 25 30 25 30 23 97
## [4,] 147 13 26 1 449 363 1000 78 1000 48
## [5,] 111 14 25 449 1 615 361 97 630 70
## [6,] 128 15 30 363 615 1 696 109 358 50
## [7,] 138 12 25 1000 361 696 1 110 488 48
## [8,] 80 22 30 78 97 109 110 1 153 34
## [9,] 119 11 23 1000 630 358 488 153 1 68
## [10,] 41 15 97 48 70 50 48 34 68 1## $n.chains
## [1] 5
##
## $n.adapt
## [1] 250000 250000 250000 250000 250000
##
## $sufficient.adapt
## [1] FALSE FALSE FALSE FALSE FALSE
##
## $n.iter
## [1] 20000
##
## $n.burnin
## [1] 0
##
## $n.thin
## [1] 100
##
## $n.samples
## [1] 1000
Environmental filtering + Facilitation sparse 20 species
JSDM
## Summary for model '/tmp/RtmpKix4lZ/file40e9629c97ad'
## Saved parameters: B Rho EnvRho
## MCMC ran in parallel for 2056.316 minutes at time 2019-04-14 05:17:59.
##
## For each of 5 chains:
## Adaptation: 250000 iterations (possibly insufficient)
## Burn-in: 0 iterations
## Thin rate: 100 iterations
## Total chain length: 270000 iterations
## Posterior sample size: 200 draws
##
## **WARNING** Rhat values indicate convergence failure.## $B
## [,1] [,2] [,3]
## [1,] 1.2009264 1.211506 1.140623
## [2,] 1.1860972 1.057014 1.212488
## [3,] 1.1480327 1.027888 1.130378
## [4,] 1.1752904 1.012028 1.161894
## [5,] 1.0163618 1.173905 1.017844
## [6,] 1.0115329 1.006395 1.026605
## [7,] 1.0091098 1.000568 1.015941
## [8,] 1.0159189 1.028463 1.011465
## [9,] 1.0147953 1.019570 1.045332
## [10,] 1.0063291 1.008805 1.009135
## [11,] 1.0231183 1.005532 1.000423
## [12,] 0.9998188 1.002953 1.004414
## [13,] 1.0040687 1.002061 1.010954
## [14,] 1.0162013 1.004407 1.000635
## [15,] 1.0217897 1.066128 1.016222
## [16,] 1.0779196 1.021127 1.124577
## [17,] 1.5653333 1.128604 1.386253
## [18,] 1.1802226 1.141001 1.278626
## [19,] 1.4282395 1.039705 1.381102
## [20,] 1.1821177 1.113667 1.153018
##
## $Rho
## [,1] [,2] [,3] [,4] [,5] [,6] [,7]
## [1,] 1.000847 1.0105264 1.0594320 1.0311957 1.2633206 1.0801668 1.1559702
## [2,] 1.010526 0.9988471 1.0347164 1.0179127 1.0613880 1.0230163 1.0307447
## [3,] 1.059432 1.0347164 0.9983805 1.0179483 1.0331487 1.0054826 1.0215763
## [4,] 1.031196 1.0179127 1.0179483 0.9985754 1.0399207 1.0145365 1.0319669
## [5,] 1.263321 1.0613880 1.0331487 1.0399207 0.9999092 1.0185258 1.0313099
## [6,] 1.080167 1.0230163 1.0054826 1.0145365 1.0185258 0.9988391 1.0187075
## [7,] 1.155970 1.0307447 1.0215763 1.0319669 1.0313099 1.0187075 0.9984636
## [8,] 1.076681 1.0181661 1.0230673 1.0621744 1.0090017 1.0207887 1.0098598
## [9,] 1.042442 1.0404052 1.0318302 1.0238873 1.0086089 1.0203758 1.0175148
## [10,] 1.112164 1.0436578 1.0102728 1.0854974 1.2184900 1.0087108 1.0261739
## [11,] 1.010825 1.0439089 1.0278774 1.0531873 1.0647847 1.0246256 1.0084867
## [12,] 1.038213 1.0241498 1.1323483 1.0561975 1.2347239 1.0971471 1.0694127
## [13,] 1.054364 1.0385368 1.1159021 1.0814599 1.0384621 1.0391679 1.0316509
## [14,] 1.020562 1.0968086 1.0532577 1.0065540 1.0727440 1.0227308 1.0382510
## [15,] 1.076461 1.0397974 1.0543872 1.0127451 1.0357231 1.0936140 1.1598696
## [16,] 1.037002 1.0267529 1.0978669 1.1654193 1.0827285 1.0418937 1.0360895
## [17,] 1.031443 1.1373285 1.0616896 1.0254841 1.0218575 1.0344634 1.0407456
## [18,] 1.031825 1.0989802 1.0318980 1.0348120 1.0294232 1.0456659 1.0569231
## [19,] 1.072367 1.0229445 1.0265228 1.0050966 1.0670184 1.0136386 1.0543381
## [20,] 1.072482 1.0522922 1.0075304 1.0197436 1.0602047 1.0477436 1.0421095
## [,8] [,9] [,10] [,11] [,12] [,13] [,14]
## [1,] 1.0766813 1.0424415 1.112164 1.010825 1.038213 1.0543645 1.0205621
## [2,] 1.0181661 1.0404052 1.043658 1.043909 1.024150 1.0385368 1.0968086
## [3,] 1.0230673 1.0318302 1.010273 1.027877 1.132348 1.1159021 1.0532577
## [4,] 1.0621744 1.0238873 1.085497 1.053187 1.056198 1.0814599 1.0065540
## [5,] 1.0090017 1.0086089 1.218490 1.064785 1.234724 1.0384621 1.0727440
## [6,] 1.0207887 1.0203758 1.008711 1.024626 1.097147 1.0391679 1.0227308
## [7,] 1.0098598 1.0175148 1.026174 1.008487 1.069413 1.0316509 1.0382510
## [8,] 0.9988498 1.0316442 1.004442 1.031162 1.009393 1.0138890 1.0018197
## [9,] 1.0316442 0.9990939 1.029507 1.058210 1.016909 1.0157517 1.0348540
## [10,] 1.0044421 1.0295070 0.999536 1.011465 1.015406 1.0311761 1.0077301
## [11,] 1.0311618 1.0582103 1.011465 0.998224 1.024463 1.0098519 1.0388046
## [12,] 1.0093932 1.0169085 1.015406 1.024463 0.998927 1.0338068 1.0320701
## [13,] 1.0138890 1.0157517 1.031176 1.009852 1.033807 0.9989222 1.0141961
## [14,] 1.0018197 1.0348540 1.007730 1.038805 1.032070 1.0141961 0.9983649
## [15,] 1.0413908 1.0884157 1.027014 1.033635 1.033843 1.0213032 1.0494409
## [16,] 1.0330935 1.0140044 1.104972 1.059808 1.009797 1.0525673 1.0074466
## [17,] 1.0964333 1.0439595 1.142587 1.153220 1.038827 1.0320922 1.0484593
## [18,] 1.0456087 1.0327120 1.028069 1.077132 1.072817 1.1519735 1.0625570
## [19,] 1.0475133 1.0818652 1.171299 1.030860 1.047409 1.0604431 1.0792875
## [20,] 1.0379662 1.0501091 1.032796 1.038673 1.099362 1.1165480 1.0658583
## [,15] [,16] [,17] [,18] [,19] [,20]
## [1,] 1.0764605 1.0370021 1.0314430 1.031825 1.0723675 1.072482
## [2,] 1.0397974 1.0267529 1.1373285 1.098980 1.0229445 1.052292
## [3,] 1.0543872 1.0978669 1.0616896 1.031898 1.0265228 1.007530
## [4,] 1.0127451 1.1654193 1.0254841 1.034812 1.0050966 1.019744
## [5,] 1.0357231 1.0827285 1.0218575 1.029423 1.0670184 1.060205
## [6,] 1.0936140 1.0418937 1.0344634 1.045666 1.0136386 1.047744
## [7,] 1.1598696 1.0360895 1.0407456 1.056923 1.0543381 1.042109
## [8,] 1.0413908 1.0330935 1.0964333 1.045609 1.0475133 1.037966
## [9,] 1.0884157 1.0140044 1.0439595 1.032712 1.0818652 1.050109
## [10,] 1.0270136 1.1049723 1.1425866 1.028069 1.1712995 1.032796
## [11,] 1.0336352 1.0598081 1.1532204 1.077132 1.0308596 1.038673
## [12,] 1.0338434 1.0097969 1.0388266 1.072817 1.0474094 1.099362
## [13,] 1.0213032 1.0525673 1.0320922 1.151974 1.0604431 1.116548
## [14,] 1.0494409 1.0074466 1.0484593 1.062557 1.0792875 1.065858
## [15,] 0.9989504 1.0339380 1.2269191 1.116314 1.1052650 1.109995
## [16,] 1.0339380 0.9980953 1.0156441 1.043229 1.0690521 1.068780
## [17,] 1.2269191 1.0156441 0.9978179 1.072580 1.0232699 1.064961
## [18,] 1.1163142 1.0432294 1.0725799 0.998882 1.0296178 1.095946
## [19,] 1.1052650 1.0690521 1.0232699 1.029618 0.9977304 1.092812
## [20,] 1.1099951 1.0687799 1.0649606 1.095946 1.0928117 NA
##
## $EnvRho
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 0.9983208 1.1114115 1.1679172 1.0515507 1.0974800 1.0708739
## [2,] 1.1114115 0.9985632 1.4330087 1.0378733 1.0583634 1.1518020
## [3,] 1.1679172 1.4330087 0.9975953 1.1585311 1.0618361 1.1149355
## [4,] 1.0515507 1.0378733 1.1585311 0.9981781 1.1935854 1.1235101
## [5,] 1.0974800 1.0583634 1.0618361 1.1935854 0.9982519 1.0301517
## [6,] 1.0708739 1.1518020 1.1149355 1.1235101 1.0301517 0.9983819
## [7,] 1.0811596 1.1013683 1.0838191 1.1468577 1.0056122 1.0194223
## [8,] 1.0643056 1.1601580 1.0513585 1.1186866 1.0239043 1.0113177
## [9,] 1.0986903 1.0983748 1.0977595 1.0954870 1.0056014 1.0164706
## [10,] 1.0685932 1.1036703 1.0652616 1.0655594 1.0131230 1.0080944
## [11,] 1.0389123 1.1273288 1.0396033 1.0874433 1.0254535 1.0112932
## [12,] 1.0533881 1.1282860 1.0641673 1.1153439 1.0229268 1.0089121
## [13,] 1.0854536 1.1221487 1.0752885 1.1072643 1.0220330 1.0183393
## [14,] 1.1121280 1.1253368 1.0734058 1.0833298 1.0194521 1.0177274
## [15,] 1.0790939 1.1209684 1.0779105 1.0973737 1.0365185 1.0119729
## [16,] 1.0614415 1.1806510 1.1004684 1.0631345 1.1458057 1.0821867
## [17,] 1.4058211 1.1052104 1.1712403 1.1719607 1.3054890 1.3240737
## [18,] 1.0696469 1.0675516 1.2424160 1.1309874 1.1608835 1.1159120
## [19,] 1.0751028 1.0420922 1.1383888 1.2513987 1.1738506 1.2700811
## [20,] 1.0951959 1.0278274 1.0410879 1.1706870 1.0682831 1.0891719
## [,7] [,8] [,9] [,10] [,11] [,12] [,13]
## [1,] 1.0811596 1.064306 1.0986903 1.0685932 1.0389123 1.0533881 1.0854536
## [2,] 1.1013683 1.160158 1.0983748 1.1036703 1.1273288 1.1282860 1.1221487
## [3,] 1.0838191 1.051358 1.0977595 1.0652616 1.0396033 1.0641673 1.0752885
## [4,] 1.1468577 1.118687 1.0954870 1.0655594 1.0874433 1.1153439 1.1072643
## [5,] 1.0056122 1.023904 1.0056014 1.0131230 1.0254535 1.0229268 1.0220330
## [6,] 1.0194223 1.011318 1.0164706 1.0080944 1.0112932 1.0089121 1.0183393
## [7,] 0.9991056 1.016641 1.0071037 1.0080521 1.0165293 1.0115380 1.0094274
## [8,] 1.0166408 0.998281 1.0212560 1.0095855 1.0003797 1.0020574 1.0091582
## [9,] 1.0071037 1.021256 0.9986401 1.0018860 1.0180396 1.0144343 1.0121478
## [10,] 1.0080521 1.009585 1.0018860 0.9995272 1.0113605 1.0111058 1.0018809
## [11,] 1.0165293 1.000380 1.0180396 1.0113605 0.9980360 0.9984231 1.0068005
## [12,] 1.0115380 1.002057 1.0144343 1.0111058 0.9984231 0.9985812 1.0101541
## [13,] 1.0094274 1.009158 1.0121478 1.0018809 1.0068005 1.0101541 0.9980014
## [14,] 1.0131252 1.008168 1.0053540 1.0008028 1.0100828 1.0127511 1.0036052
## [15,] 1.0259815 1.009301 1.0227359 1.0105417 1.0015252 1.0030144 1.0091385
## [16,] 1.0910810 1.065146 1.0741038 1.0409416 1.0433115 1.0563553 1.0607146
## [17,] 1.3447248 1.217563 1.2950608 1.2543017 1.1489788 1.1797843 1.3066543
## [18,] 1.1898546 1.107894 1.1796378 1.1251056 1.0655662 1.0980767 1.1718814
## [19,] 1.2282162 1.218350 1.1688814 1.1834992 1.1919342 1.2401654 1.2418950
## [20,] 1.0967294 1.120749 1.0597162 1.0601469 1.1040554 1.1067132 1.0897829
## [,14] [,15] [,16] [,17] [,18] [,19] [,20]
## [1,] 1.1121280 1.079094 1.0614415 1.4058211 1.069647 1.075103 1.095196
## [2,] 1.1253368 1.120968 1.1806510 1.1052104 1.067552 1.042092 1.027827
## [3,] 1.0734058 1.077910 1.1004684 1.1712403 1.242416 1.138389 1.041088
## [4,] 1.0833298 1.097374 1.0631345 1.1719607 1.130987 1.251399 1.170687
## [5,] 1.0194521 1.036519 1.1458057 1.3054890 1.160883 1.173851 1.068283
## [6,] 1.0177274 1.011973 1.0821867 1.3240737 1.115912 1.270081 1.089172
## [7,] 1.0131252 1.025981 1.0910810 1.3447248 1.189855 1.228216 1.096729
## [8,] 1.0081681 1.009301 1.0651457 1.2175628 1.107894 1.218350 1.120749
## [9,] 1.0053540 1.022736 1.0741038 1.2950608 1.179638 1.168881 1.059716
## [10,] 1.0008028 1.010542 1.0409416 1.2543017 1.125106 1.183499 1.060147
## [11,] 1.0100828 1.001525 1.0433115 1.1489788 1.065566 1.191934 1.104055
## [12,] 1.0127511 1.003014 1.0563553 1.1797843 1.098077 1.240165 1.106713
## [13,] 1.0036052 1.009138 1.0607146 1.3066543 1.171881 1.241895 1.089783
## [14,] 0.9984386 1.018893 1.0429691 1.2955036 1.165180 1.216233 1.089638
## [15,] 1.0188931 0.997824 1.0626465 1.2283584 1.101777 1.296548 1.100136
## [16,] 1.0429691 1.062647 0.9979461 1.4826346 1.073386 1.341845 1.121754
## [17,] 1.2955036 1.228358 1.4826346 0.9996401 1.113600 1.368712 1.385371
## [18,] 1.1651799 1.101777 1.0733860 1.1136004 0.998105 1.262442 1.162356
## [19,] 1.2162327 1.296548 1.3418450 1.3687115 1.262442 0.998575 1.299824
## [20,] 1.0896376 1.100136 1.1217538 1.3853705 1.162356 1.299824 0.998583## $B
## [,1] [,2] [,3]
## [1,] 24 21 32
## [2,] 22 59 19
## [3,] 27 108 32
## [4,] 22 251 24
## [5,] 227 23 216
## [6,] 273 395 140
## [7,] 351 1000 250
## [8,] 177 104 232
## [9,] 206 144 69
## [10,] 384 290 280
## [11,] 128 404 1000
## [12,] 1000 570 505
## [13,] 887 747 270
## [14,] 170 1000 1000
## [15,] 175 51 217
## [16,] 43 147 29
## [17,] 10 29 13
## [18,] 22 26 16
## [19,] 12 84 13
## [20,] 23 33 25
##
## $Rho
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
## [1,] 1 422 57 96 17 43 24 74 81 32 502 101 59
## [2,] 422 1 91 212 54 128 110 167 93 75 89 128 87
## [3,] 57 91 1 172 94 434 162 154 111 298 118 29 33
## [4,] 96 212 172 1 92 399 138 52 123 41 74 65 43
## [5,] 17 54 94 92 1 164 95 612 312 19 71 18 95
## [6,] 43 128 434 399 164 1 152 162 180 349 118 37 80
## [7,] 24 110 162 138 95 152 1 379 185 129 435 48 101
## [8,] 74 167 154 52 612 162 379 1 105 519 122 477 204
## [9,] 81 93 111 123 312 180 185 105 1 112 61 263 190
## [10,] 32 75 298 41 19 349 129 519 112 1 247 184 102
## [11,] 502 89 118 74 71 118 435 122 61 247 1 120 301
## [12,] 101 128 29 65 18 37 48 477 263 184 120 1 92
## [13,] 59 87 33 43 95 80 101 204 190 102 301 92 1
## [14,] 150 38 72 603 57 373 83 739 91 354 80 100 193
## [15,] 45 80 61 273 90 38 24 82 39 153 99 95 137
## [16,] 92 111 36 23 43 83 107 95 198 34 55 302 66
## [17,] 97 28 53 253 137 244 115 38 170 26 25 104 118
## [18,] 107 38 109 107 103 72 57 73 98 121 44 47 27
## [19,] 46 150 110 517 52 216 66 81 42 23 137 80 54
## [20,] 51 66 339 154 64 75 85 98 68 98 82 40 31
## [,14] [,15] [,16] [,17] [,18] [,19] [,20]
## [1,] 150 45 92 97 107 46 51
## [2,] 38 80 111 28 38 150 66
## [3,] 72 61 36 53 109 110 339
## [4,] 603 273 23 253 107 517 154
## [5,] 57 90 43 137 103 52 64
## [6,] 373 38 83 244 72 216 75
## [7,] 83 24 107 115 57 66 85
## [8,] 739 82 95 38 73 81 98
## [9,] 91 39 198 170 98 42 68
## [10,] 354 153 34 26 121 23 98
## [11,] 80 99 55 25 44 137 82
## [12,] 100 95 302 104 47 80 40
## [13,] 193 137 66 118 27 54 31
## [14,] 1 69 337 73 53 44 61
## [15,] 69 1 88 19 33 36 34
## [16,] 337 88 1 210 83 48 51
## [17,] 73 19 210 1 51 125 51
## [18,] 53 33 83 51 1 123 37
## [19,] 44 36 48 125 123 1 37
## [20,] 61 34 51 51 37 37 1
##
## $EnvRho
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
## [1,] 1 42 28 93 37 48 50 58 36 53 87 66 47
## [2,] 42 1 15 128 56 25 37 24 36 35 29 28 29
## [3,] 28 15 1 29 57 32 42 62 37 51 84 56 50
## [4,] 93 128 29 1 25 32 26 32 37 50 39 31 33
## [5,] 37 56 57 25 1 137 521 146 787 251 123 139 141
## [6,] 48 25 32 32 137 1 536 243 205 335 240 303 169
## [7,] 50 37 42 26 521 536 1 189 402 349 214 254 319
## [8,] 58 24 62 32 146 243 189 1 251 258 1000 1000 283
## [9,] 36 36 37 37 787 205 402 251 1 705 162 189 266
## [10,] 53 35 51 50 251 335 349 258 705 1 244 246 1000
## [11,] 87 29 84 39 123 240 214 1000 162 244 1 1000 367
## [12,] 66 28 56 31 139 303 254 1000 189 246 1000 1 324
## [13,] 47 29 50 33 141 169 319 283 266 1000 367 324 1
## [14,] 36 28 50 43 160 187 217 312 412 1000 276 246 749
## [15,] 55 31 48 36 89 301 132 550 140 294 1000 1000 305
## [16,] 72 22 40 57 26 43 37 50 46 76 73 56 58
## [17,] 17 47 24 37 15 15 14 20 15 17 26 23 15
## [18,] 76 74 21 51 23 30 21 33 22 29 51 36 23
## [19,] 108 155 40 28 32 17 19 19 24 22 21 18 18
## [20,] 41 182 143 38 68 42 39 31 55 57 34 33 38
## [,14] [,15] [,16] [,17] [,18] [,19] [,20]
## [1,] 36 55 72 17 76 108 41
## [2,] 28 31 22 47 74 155 182
## [3,] 50 48 40 24 21 40 143
## [4,] 43 36 57 37 51 28 38
## [5,] 160 89 26 15 23 32 68
## [6,] 187 301 43 15 30 17 42
## [7,] 217 132 37 14 21 19 39
## [8,] 312 550 50 20 33 19 31
## [9,] 412 140 46 15 22 24 55
## [10,] 1000 294 76 17 29 22 57
## [11,] 276 1000 73 26 51 21 34
## [12,] 246 1000 56 23 36 18 33
## [13,] 749 305 58 15 23 18 38
## [14,] 1 255 75 16 24 19 39
## [15,] 255 1 125 19 38 15 36
## [16,] 75 125 1 13 48 14 30
## [17,] 16 19 13 1 103 14 14
## [18,] 24 38 48 103 1 18 25
## [19,] 19 15 14 14 18 1 21
## [20,] 39 36 30 14 25 21 1## $n.chains
## [1] 5
##
## $n.adapt
## [1] 250000 250000 250000 250000 250000
##
## $sufficient.adapt
## [1] FALSE FALSE FALSE FALSE FALSE
##
## $n.iter
## [1] 20000
##
## $n.burnin
## [1] 0
##
## $n.thin
## [1] 100
##
## $n.samples
## [1] 1000
Environmental filtering + Competition dense 5 species
JSDM
## Summary for model '/tmp/RtmpKix4lZ/file40e9127bbb96'
## Saved parameters: B Rho EnvRho
## MCMC ran in parallel for 29.308 minutes at time 2019-04-15 15:34:20.
##
## For each of 5 chains:
## Adaptation: 250000 iterations (possibly insufficient)
## Burn-in: 0 iterations
## Thin rate: 100 iterations
## Total chain length: 270000 iterations
## Posterior sample size: 200 draws
##
## Successful convergence based on Rhat values (all < 1.1).## $B
## [,1] [,2] [,3]
## [1,] 0.9992699 1.0039019 1.0085401
## [2,] 1.0128736 1.0070599 1.0090347
## [3,] 1.0084590 0.9998466 1.0094351
## [4,] 0.9994956 0.9994269 0.9994353
## [5,] 0.9990868 1.0059362 1.0323796
##
## $Rho
## [,1] [,2] [,3] [,4] [,5]
## [1,] 0.9997647 1.0111732 1.0136253 1.0262793 1.057643
## [2,] 1.0111732 0.9986603 1.0244584 1.0067418 1.067133
## [3,] 1.0136253 1.0244584 0.9983207 1.0027597 1.033900
## [4,] 1.0262793 1.0067418 1.0027597 0.9990709 1.018522
## [5,] 1.0576430 1.0671326 1.0339003 1.0185219 NA
##
## $EnvRho
## [,1] [,2] [,3] [,4] [,5]
## [1,] 0.9985283 1.0042437 1.0066617 1.0130860 1.0157941
## [2,] 1.0042437 0.9976154 1.0015341 1.0045804 1.0241824
## [3,] 1.0066617 1.0015341 0.9986805 0.9995707 1.0221389
## [4,] 1.0130860 1.0045804 0.9995707 0.9984863 1.0312755
## [5,] 1.0157941 1.0241824 1.0221389 1.0312755 0.9979406## $B
## [,1] [,2] [,3]
## [1,] 1000 1000 389
## [2,] 210 355 355
## [3,] 320 1000 272
## [4,] 1000 1000 1000
## [5,] 1000 463 95
##
## $Rho
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1 252 209 114 59
## [2,] 252 1 132 483 52
## [3,] 209 132 1 611 96
## [4,] 114 483 611 1 162
## [5,] 59 52 96 162 1
##
## $EnvRho
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1 556 455 431 207
## [2,] 556 1 795 661 120
## [3,] 455 795 1 1000 130
## [4,] 431 661 1000 1 100
## [5,] 207 120 130 100 1## $n.chains
## [1] 5
##
## $n.adapt
## [1] 250000 250000 250000 250000 250000
##
## $sufficient.adapt
## [1] FALSE FALSE FALSE FALSE FALSE
##
## $n.iter
## [1] 20000
##
## $n.burnin
## [1] 0
##
## $n.thin
## [1] 100
##
## $n.samples
## [1] 1000
Environmental filtering + Competition dense 10 species
## Summary for model '/tmp/RtmpKix4lZ/file40e9559ce9d8'
## Saved parameters: B Rho EnvRho
## MCMC ran in parallel for 695.614 minutes at time 2019-04-15 16:03:39.
##
## For each of 5 chains:
## Adaptation: 250000 iterations (possibly insufficient)
## Burn-in: 0 iterations
## Thin rate: 100 iterations
## Total chain length: 270000 iterations
## Posterior sample size: 200 draws
##
## **WARNING** Rhat values indicate convergence failure.## $B
## [,1] [,2] [,3]
## [1,] 1.184491 1.0924718 1.174168
## [2,] 1.002562 1.0199804 1.032937
## [3,] 1.004197 1.0037121 1.003388
## [4,] 1.003017 1.0063025 1.003671
## [5,] 1.001863 1.0028406 1.003823
## [6,] 1.003347 0.9994502 1.007329
## [7,] 1.005931 1.0148814 1.008707
## [8,] 1.133921 1.1394220 1.120682
## [9,] 1.036365 1.0229201 1.042030
## [10,] 1.014222 1.0252783 1.032749
##
## $Rho
## [,1] [,2] [,3] [,4] [,5] [,6] [,7]
## [1,] 0.9982981 1.0736358 1.0163397 1.0389520 1.0391268 1.028132 1.0342467
## [2,] 1.0736358 0.9982061 1.0855782 1.0379838 1.0106180 1.090370 1.0482529
## [3,] 1.0163397 1.0855782 0.9990348 1.0100777 1.0058427 1.009966 1.0187807
## [4,] 1.0389520 1.0379838 1.0100777 0.9976574 1.0388340 1.024137 1.0056891
## [5,] 1.0391268 1.0106180 1.0058427 1.0388340 0.9985805 1.004055 1.0260171
## [6,] 1.0281324 1.0903700 1.0099655 1.0241372 1.0040550 0.997530 1.0135124
## [7,] 1.0342467 1.0482529 1.0187807 1.0056891 1.0260171 1.013512 0.9977998
## [8,] 1.0162888 1.0194622 1.0142569 1.0749787 1.0254736 1.020784 1.0640900
## [9,] 1.0177287 1.0434650 1.0035099 1.0273927 1.0569636 1.012938 1.0127183
## [10,] 1.0241698 1.0189370 1.0141069 1.0063696 1.0130281 1.022538 1.0905019
## [,8] [,9] [,10]
## [1,] 1.0162888 1.0177287 1.024170
## [2,] 1.0194622 1.0434650 1.018937
## [3,] 1.0142569 1.0035099 1.014107
## [4,] 1.0749787 1.0273927 1.006370
## [5,] 1.0254736 1.0569636 1.013028
## [6,] 1.0207836 1.0129383 1.022538
## [7,] 1.0640900 1.0127183 1.090502
## [8,] 0.9986921 1.0073322 1.020647
## [9,] 1.0073322 0.9986258 1.017626
## [10,] 1.0206467 1.0176263 NA
##
## $EnvRho
## [,1] [,2] [,3] [,4] [,5] [,6] [,7]
## [1,] 0.9980917 1.1132907 1.116068 1.0538487 1.0579563 1.1174585 1.1770617
## [2,] 1.1132907 0.9993671 1.032695 1.0376641 1.0367794 1.0288659 1.0240739
## [3,] 1.1160684 1.0326949 0.998218 1.0067471 1.0039030 1.0036538 1.0288561
## [4,] 1.0538487 1.0376641 1.006747 0.9992088 0.9997989 1.0071268 1.0291796
## [5,] 1.0579563 1.0367794 1.003903 0.9997989 0.9978700 1.0064940 1.0256165
## [6,] 1.1174585 1.0288659 1.003654 1.0071268 1.0064940 0.9979281 1.0108040
## [7,] 1.1770617 1.0240739 1.028856 1.0291796 1.0256165 1.0108040 0.9980596
## [8,] 1.0269566 1.0168974 1.106804 1.0785522 1.0895479 1.0590564 1.0315088
## [9,] 1.1552814 1.0340803 1.041486 1.0330602 1.0193857 1.0291867 1.0513752
## [10,] 1.0726914 1.0117231 1.036152 1.0362888 1.0314847 1.0196400 1.0156937
## [,8] [,9] [,10]
## [1,] 1.0269566 1.1552814 1.0726914
## [2,] 1.0168974 1.0340803 1.0117231
## [3,] 1.1068043 1.0414856 1.0361524
## [4,] 1.0785522 1.0330602 1.0362888
## [5,] 1.0895479 1.0193857 1.0314847
## [6,] 1.0590564 1.0291867 1.0196400
## [7,] 1.0315088 1.0513752 1.0156937
## [8,] 0.9989128 1.0795098 1.0555299
## [9,] 1.0795098 0.9981866 1.0369466
## [10,] 1.0555299 1.0369466 0.9987184## $B
## [,1] [,2] [,3]
## [1,] 24 38 25
## [2,] 802 157 93
## [3,] 833 870 1000
## [4,] 573 462 566
## [5,] 994 601 648
## [6,] 708 1000 329
## [7,] 420 240 320
## [8,] 29 28 33
## [9,] 123 174 114
## [10,] 206 117 100
##
## $Rho
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 1 49 197 83 87 125 89 176 184 124
## [2,] 49 1 40 82 315 39 73 187 71 174
## [3,] 197 40 1 387 377 273 177 209 857 269
## [4,] 83 82 387 1 81 136 1000 50 131 442
## [5,] 87 315 377 81 1 1000 118 169 59 219
## [6,] 125 39 273 136 1000 1 1000 144 242 144
## [7,] 89 73 177 1000 118 1000 1 55 216 41
## [8,] 176 187 209 50 169 144 55 1 381 155
## [9,] 184 71 857 131 59 242 216 381 1 175
## [10,] 124 174 269 442 219 144 41 155 175 1
##
## $EnvRho
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 1 39 33 59 59 34 24 134 31 59
## [2,] 39 1 96 83 84 108 170 178 92 306
## [3,] 33 96 1 509 591 612 105 37 105 96
## [4,] 59 83 509 1 1000 326 105 44 114 97
## [5,] 59 84 591 1000 1 385 119 39 181 99
## [6,] 34 108 612 326 385 1 256 59 112 172
## [7,] 24 170 105 105 119 256 1 113 67 194
## [8,] 134 178 37 44 39 59 113 1 69 222
## [9,] 31 92 105 114 181 112 67 69 1 123
## [10,] 59 306 96 97 99 172 194 222 123 1## $n.chains
## [1] 5
##
## $n.adapt
## [1] 250000 250000 250000 250000 250000
##
## $sufficient.adapt
## [1] FALSE FALSE FALSE FALSE FALSE
##
## $n.iter
## [1] 20000
##
## $n.burnin
## [1] 0
##
## $n.thin
## [1] 100
##
## $n.samples
## [1] 1000
Environmental filtering + Competition dense 20 species
Environmental filtering + Competition sparse 5 species
Environmental filtering + Competition dense 10 species
Environmental filtering + Competition dense 5 species
Visualization for true interactions
